1 00:00:00,000 --> 00:00:07,680 Welcome to the Cannabis Data Science Meetup Group. 2 00:00:07,680 --> 00:00:09,440 Thrilled to have you all here. 3 00:00:09,440 --> 00:00:14,520 This is a roundtable for all of you brilliant minds 4 00:00:14,520 --> 00:00:17,120 to come together. 5 00:00:17,120 --> 00:00:18,560 I'll join you too. 6 00:00:18,560 --> 00:00:24,200 And we can talk about cannabis data, various scientific ways 7 00:00:24,200 --> 00:00:26,640 that we can analyze the data. 8 00:00:26,640 --> 00:00:27,760 Why? 9 00:00:27,760 --> 00:00:32,320 Well, the cannabis industry has been moving forward. 10 00:00:32,320 --> 00:00:34,560 It's going through turbulent times. 11 00:00:34,560 --> 00:00:41,640 And the way I say is we can let the politicians politic. 12 00:00:41,640 --> 00:00:45,040 We can let the lawyers that practice law and all of that 13 00:00:45,040 --> 00:00:45,800 jazz. 14 00:00:45,800 --> 00:00:48,240 What can we add to the table? 15 00:00:48,240 --> 00:00:50,640 We can calculate statistics. 16 00:00:50,640 --> 00:00:53,440 And I think that adds an extraordinary amount 17 00:00:53,440 --> 00:00:55,080 to the picture. 18 00:00:55,080 --> 00:00:58,400 So today, once again, we'll put a bunch of really cool 19 00:00:58,400 --> 00:01:01,000 statistics in your pocket. 20 00:01:01,000 --> 00:01:02,240 Because that's what I do. 21 00:01:02,240 --> 00:01:04,960 I just start calculating most basic statistics 22 00:01:04,960 --> 00:01:06,360 and work my way up. 23 00:01:06,360 --> 00:01:09,240 And I love presenting to all of you 24 00:01:09,240 --> 00:01:12,960 because I actually want your red ink. 25 00:01:12,960 --> 00:01:15,720 So please be super critical of my work 26 00:01:15,720 --> 00:01:19,200 because this is just like a first draft, a rough draft 27 00:01:19,200 --> 00:01:20,920 of some data analytics. 28 00:01:20,920 --> 00:01:23,400 And the way any good writer gets better 29 00:01:23,400 --> 00:01:26,880 is by getting critiqued by an editor. 30 00:01:26,880 --> 00:01:30,520 So please view yourselves as critical editors. 31 00:01:30,520 --> 00:01:32,720 And then if you ever have any research 32 00:01:32,720 --> 00:01:35,640 that you want to bring here and get critiqued 33 00:01:35,640 --> 00:01:40,200 by a friendly group of peers, you're welcome to. 34 00:01:40,200 --> 00:01:42,920 So that's sort of the mission of the group. 35 00:01:42,920 --> 00:01:47,600 So just going to, if you want, I always do this. 36 00:01:47,600 --> 00:01:49,560 Let me know if this is ever painful. 37 00:01:49,560 --> 00:01:52,160 But I always find it's pretty fun just to go around 38 00:01:52,160 --> 00:01:55,520 and let everybody just maybe say a quick word for themselves 39 00:01:55,520 --> 00:01:58,480 and maybe what you're working on this week 40 00:01:58,480 --> 00:02:02,800 or anything you want to see added to the table, 41 00:02:02,800 --> 00:02:06,880 any data that you're after, anything at all. 42 00:02:06,880 --> 00:02:11,880 So I may actually go in reverse order today 43 00:02:13,000 --> 00:02:14,880 if that's OK with everybody. 44 00:02:14,880 --> 00:02:18,240 Just kind of interested. 45 00:02:18,240 --> 00:02:23,240 And so Ruth, if you've got your microphone set up, 46 00:02:25,160 --> 00:02:28,280 we'd love to hear about some of the things 47 00:02:28,280 --> 00:02:29,400 you want to bring to the picture. 48 00:02:29,400 --> 00:02:33,520 That way we can let some of the newer members speak 49 00:02:33,520 --> 00:02:36,960 and then we can let the veterans have their say. 50 00:02:41,160 --> 00:02:44,160 Also, you may just be muted this time around. 51 00:02:44,160 --> 00:02:46,440 OK, there we go. 52 00:02:46,440 --> 00:02:49,240 No real data issues to report. 53 00:02:49,240 --> 00:02:53,240 I'm giving a talk tomorrow on evolution of science 54 00:02:53,240 --> 00:02:57,560 and technology in cannabis and how I think the research 55 00:02:57,560 --> 00:03:02,560 into medical cannabis is going to end up being like penicillin 56 00:03:02,560 --> 00:03:06,400 and it will have wide-ranging implications 57 00:03:06,400 --> 00:03:10,760 for many other disciplines and throughout society. 58 00:03:10,760 --> 00:03:12,560 So I'm excited about that. 59 00:03:12,560 --> 00:03:15,240 But no real data work to report. 60 00:03:16,760 --> 00:03:19,200 It's cool that you bring that up because we can't forget 61 00:03:19,200 --> 00:03:20,800 about the medical side of the picture. 62 00:03:20,800 --> 00:03:24,960 And as I'll show you today, I don't think it's going anywhere. 63 00:03:24,960 --> 00:03:31,960 You've got the tiniest bit of data points 64 00:03:31,960 --> 00:03:34,360 that we can actually begin to actually uncover 65 00:03:34,360 --> 00:03:36,360 a little bit about medical cannabis. 66 00:03:36,360 --> 00:03:39,680 So I just scraped the surface so there's actually more for you 67 00:03:39,680 --> 00:03:42,680 to dive into. 68 00:03:42,680 --> 00:03:44,680 So cool data today. 69 00:03:44,680 --> 00:03:46,680 Thanks for joining me. 70 00:03:46,680 --> 00:03:51,680 Kayla, I know you were interested in strains. 71 00:03:51,680 --> 00:03:54,680 You're running copyright, not copyright, 72 00:03:54,680 --> 00:03:57,680 copyright left cultivars. 73 00:03:57,680 --> 00:04:02,680 Coincidentally, we'll be talking about a few cultivars today. 74 00:04:02,680 --> 00:04:04,680 We'd love to go a bit further in depth. 75 00:04:04,680 --> 00:04:07,680 So once again, we'll just scrape the surface. 76 00:04:07,680 --> 00:04:10,680 So this is something you can go much deeper on. 77 00:04:10,680 --> 00:04:12,680 But once again, we'd love to hear about what you want to put 78 00:04:12,680 --> 00:04:15,680 on the table for today. 79 00:04:15,680 --> 00:04:17,680 Cool. Yeah, thanks. 80 00:04:17,680 --> 00:04:22,680 Well, I've just been working on refining the intake system 81 00:04:22,680 --> 00:04:26,680 for the backend, the database and for the galaxy 82 00:04:26,680 --> 00:04:27,680 that we're working on. 83 00:04:27,680 --> 00:04:30,680 We're working on a copyleft protected open source 84 00:04:30,680 --> 00:04:34,680 public collaboration galaxy where we can have systems 85 00:04:34,680 --> 00:04:37,680 for open collaboration and breeding as well as sharing 86 00:04:37,680 --> 00:04:42,680 trait and gene information that can lead towards openly sharing 87 00:04:42,680 --> 00:04:44,680 the benefits of that in breeding, 88 00:04:44,680 --> 00:04:47,680 in understanding the meaning of the genes, 89 00:04:47,680 --> 00:04:49,680 and even like being able to build that out. 90 00:04:49,680 --> 00:04:52,680 So I've been working kind of on the backend there. 91 00:04:52,680 --> 00:04:53,680 So I'm a little out of breath. 92 00:04:53,680 --> 00:04:55,680 I just finished my workout right before this. 93 00:04:55,680 --> 00:05:00,680 But anyways, we're also working with this app that the 94 00:05:00,680 --> 00:05:05,680 University of Kansas put together and modifying that in tandem 95 00:05:05,680 --> 00:05:09,680 with them to be usable in the field for cannabis growers 96 00:05:09,680 --> 00:05:12,680 to document all that information that I just said, 97 00:05:12,680 --> 00:05:13,680 get it into our galaxy. 98 00:05:13,680 --> 00:05:16,680 So I've been pretty deep into that work this last week, 99 00:05:16,680 --> 00:05:22,680 as well as we're building a bot, an AI-powered chat bot 100 00:05:22,680 --> 00:05:26,680 that has natural farming and Frickshark beta and similar 101 00:05:26,680 --> 00:05:31,680 information there, working with people like maybe in Thailand 102 00:05:31,680 --> 00:05:35,680 and Ghana, other places to see if we can hone it in for their 103 00:05:35,680 --> 00:05:40,680 farmers, cannabis included, but that's more broadly just for 104 00:05:40,680 --> 00:05:45,680 plants in general and more organic farming techniques. 105 00:05:45,680 --> 00:05:48,680 So yeah, that's really what I've been deep on. 106 00:05:48,680 --> 00:05:51,680 I'm excited to see the data though and chit chat. 107 00:05:51,680 --> 00:05:53,680 And you know, I love networking in this space and building 108 00:05:53,680 --> 00:05:56,680 these connections, moving this open science along. 109 00:05:56,680 --> 00:06:00,680 So thanks for bringing us all here to collaborate. 110 00:06:00,680 --> 00:06:01,680 I love it. 111 00:06:01,680 --> 00:06:05,680 Tons of ideas, but I'll let Ruth chime in so I don't steal 112 00:06:05,680 --> 00:06:06,680 the show. 113 00:06:06,680 --> 00:06:07,680 Ruth, what do you think? 114 00:06:07,680 --> 00:06:09,680 That's really interesting. 115 00:06:09,680 --> 00:06:13,680 I'm interested in understanding our, if you're looking at 116 00:06:13,680 --> 00:06:17,680 strain genetics and profiles and stuff, are you seeing an 117 00:06:17,680 --> 00:06:19,680 increase in variety or decrease in variety? 118 00:06:19,680 --> 00:06:23,680 So are we seeing essentially more incestuousness or are we 119 00:06:23,680 --> 00:06:27,680 actually getting more variety out there? 120 00:06:27,680 --> 00:06:28,680 So that's a great question. 121 00:06:28,680 --> 00:06:31,680 That's one of the questions that we hope to answer. 122 00:06:31,680 --> 00:06:35,680 But the real down in the dirt answer is that we just don't 123 00:06:35,680 --> 00:06:36,680 know. 124 00:06:36,680 --> 00:06:39,680 And there's even like been a number of papers attempting to 125 00:06:39,680 --> 00:06:42,680 do this that in the papers tend to also admit that we just 126 00:06:42,680 --> 00:06:44,680 don't have the data to know that. 127 00:06:44,680 --> 00:06:47,680 And so a lot of what we're trying to do at Copyloaf 128 00:06:47,680 --> 00:06:51,680 is just create the systems where the community can then get 129 00:06:51,680 --> 00:06:54,680 the data together and then we can figure out the real answers 130 00:06:54,680 --> 00:06:57,680 to those questions and more questions. 131 00:06:57,680 --> 00:07:00,680 Now, are you looking at data just within the country? 132 00:07:00,680 --> 00:07:03,680 I mean, you mentioned like Ghana and I mean, certainly in a 133 00:07:03,680 --> 00:07:05,680 lot of areas in Europe. 134 00:07:05,680 --> 00:07:10,680 I've seen studies done and they tend to focus on, you know, 135 00:07:10,680 --> 00:07:14,680 there's studies done out of, was it like Amsterdam or Sweden 136 00:07:14,680 --> 00:07:18,680 or something trying to look at kind of the variety in the 137 00:07:18,680 --> 00:07:19,680 plants there. 138 00:07:19,680 --> 00:07:23,680 But they tend to go into one area and get a sampling there. 139 00:07:23,680 --> 00:07:26,680 And I'd be interested almost like in a global sampling, is 140 00:07:26,680 --> 00:07:28,680 anything like that being done? 141 00:07:28,680 --> 00:07:31,680 Yeah, it seems like you're thinking along the same line 142 00:07:31,680 --> 00:07:32,680 that we're doing. 143 00:07:32,680 --> 00:07:36,680 So our perspective is a little bit different, you know, 144 00:07:36,680 --> 00:07:37,680 talking. 145 00:07:37,680 --> 00:07:42,680 We kind of came out of a community series of meetings, 146 00:07:42,680 --> 00:07:46,680 open meetings, and there were researchers, there were people 147 00:07:46,680 --> 00:07:48,680 who had kind of been burned out by the phylos incident. 148 00:07:48,680 --> 00:07:50,680 There were like all of these different interests. 149 00:07:50,680 --> 00:07:55,680 There were people from the highlands of Tibet and in 150 00:07:55,680 --> 00:07:56,680 Thailand. 151 00:07:56,680 --> 00:07:58,680 And so we have like lots of different influences in the 152 00:07:58,680 --> 00:08:01,680 formation and we decided that our approach should be entirely 153 00:08:01,680 --> 00:08:02,680 open. 154 00:08:02,680 --> 00:08:05,680 And so we're not going in and getting a data set. 155 00:08:05,680 --> 00:08:09,680 We're creating a system that anyone can put their data into 156 00:08:09,680 --> 00:08:12,680 and know that it won't be hijacked, that it will be kept in 157 00:08:12,680 --> 00:08:16,680 these open source systems where we can't even, as the holders 158 00:08:16,680 --> 00:08:20,680 of it, lock it down because of the licensing terms. 159 00:08:20,680 --> 00:08:23,680 Can you put a link to kind of your site or whatever it is 160 00:08:23,680 --> 00:08:25,680 that you're doing into the notes? 161 00:08:25,680 --> 00:08:27,680 So I would love to share that with other people. 162 00:08:27,680 --> 00:08:30,680 I'm sure a lot of people would be very interested in that. 163 00:08:30,680 --> 00:08:33,680 And thank you very much for doing that. 164 00:08:33,680 --> 00:08:35,680 Oh, yeah, absolutely. 165 00:08:35,680 --> 00:08:37,680 It's a big passion and also it's a lot of fun. 166 00:08:37,680 --> 00:08:40,680 And yeah, it's copyleftcultivars.com. 167 00:08:40,680 --> 00:08:43,680 So I'll put that there in the chat. 168 00:08:43,680 --> 00:08:48,680 Yeah, we're super just open collaboration themed. 169 00:08:48,680 --> 00:08:50,680 So feel free to reach out. 170 00:08:50,680 --> 00:08:54,680 You know, I don't want to drag the meeting too far into kind of 171 00:08:54,680 --> 00:08:57,680 recruiting or networking for the nonprofit because I know we have 172 00:08:57,680 --> 00:09:00,680 some really cool information also that Keegan has brought. 173 00:09:00,680 --> 00:09:05,680 But yeah, I want to be super open also to anybody who wants to 174 00:09:05,680 --> 00:09:07,680 jump in and be involved with the work we're doing. 175 00:09:07,680 --> 00:09:12,680 This is the fun thing about the meetup because we can actually go 176 00:09:12,680 --> 00:09:13,680 off road. 177 00:09:13,680 --> 00:09:17,680 So right, there's no reason we have to stay on the agenda. 178 00:09:17,680 --> 00:09:21,680 We don't have to stay on the trail. 179 00:09:21,680 --> 00:09:25,680 And I think you're doing a much more rigorous job at this because 180 00:09:25,680 --> 00:09:30,680 Ruth asked a super interesting question, which is, you know, 181 00:09:30,680 --> 00:09:33,680 what's going on with the diversity in cannabis? 182 00:09:33,680 --> 00:09:36,680 Like, so I guess you would think genetic diversity. 183 00:09:36,680 --> 00:09:39,680 So, of course, you need to do some genetic testing. 184 00:09:39,680 --> 00:09:46,680 But what came to my mind is we could estimate perhaps what's 185 00:09:46,680 --> 00:09:47,680 going on. 186 00:09:47,680 --> 00:09:50,680 And once again, we don't have genetic data, but we have strain 187 00:09:50,680 --> 00:09:51,680 data. 188 00:09:51,680 --> 00:09:54,680 And so that's what I'll share with you today. 189 00:09:54,680 --> 00:09:59,680 And so what we could just do, this is more of just a product count 190 00:09:59,680 --> 00:10:04,680 versus diversity count, but you could just count all the unique 191 00:10:04,680 --> 00:10:10,680 strains that are tested in any given month and see if that's 192 00:10:10,680 --> 00:10:11,680 going up or down. 193 00:10:11,680 --> 00:10:15,680 Once again, that doesn't necessarily mean diversity is going 194 00:10:15,680 --> 00:10:18,680 up or down because you may just have, and I'll show you later, 195 00:10:18,680 --> 00:10:22,680 you may have like runts and white runts and yellow runts and all 196 00:10:22,680 --> 00:10:23,680 of that jazz. 197 00:10:23,680 --> 00:10:28,680 So just because there's a proliferate variation of strain 198 00:10:28,680 --> 00:10:31,680 names doesn't mean diversity is going up or down. 199 00:10:31,680 --> 00:10:34,680 But any, Yasha, you have some thoughts? 200 00:10:34,680 --> 00:10:37,680 Yeah, I want to second what everyone else is saying. 201 00:10:37,680 --> 00:10:41,680 I think what you're working on is really cool. 202 00:10:41,680 --> 00:10:45,680 And Copyleft is an excellent name. 203 00:10:45,680 --> 00:10:50,680 And to back up what Keegan is saying, I really like the concept 204 00:10:50,680 --> 00:10:53,680 of when you have something that you're the first to measure, 205 00:10:53,680 --> 00:10:56,680 if you can measure it from multiple different approaches, 206 00:10:56,680 --> 00:11:00,680 one is from the data and strain names, another one could be from 207 00:11:00,680 --> 00:11:05,680 the bouquet of compounds found within the open data, 208 00:11:05,680 --> 00:11:08,680 a third is genetic testing. 209 00:11:08,680 --> 00:11:11,680 I don't know what else, maybe just like culturally, what are 210 00:11:11,680 --> 00:11:14,680 people talking about more, what comes up more as mentioned in 211 00:11:14,680 --> 00:11:20,680 terms of strains, but there may be multiple sources that point 212 00:11:20,680 --> 00:11:25,680 at the same thing that could make the work that much more 213 00:11:25,680 --> 00:11:30,680 certain. 214 00:11:30,680 --> 00:11:33,680 I like the idea of having compounds as identifiers. 215 00:11:33,680 --> 00:11:34,680 That's a great one. 216 00:11:34,680 --> 00:11:37,680 Yeah, we're actually currently working on with the app trying 217 00:11:37,680 --> 00:11:42,680 to figure out how we make those parts collectible, so to speak, 218 00:11:42,680 --> 00:11:46,680 since it was built for like corn, for example, field crops 219 00:11:46,680 --> 00:11:47,680 like that. 220 00:11:47,680 --> 00:11:53,680 But yeah, I love that as a possible identifier as well. 221 00:11:53,680 --> 00:11:56,680 And yeah, the strain name idea is a great one, and I think you 222 00:11:56,680 --> 00:12:00,680 can also use tools like Seed Finder or like we have a project 223 00:12:00,680 --> 00:12:03,680 going where we're web scraping Seed Finder and a bunch of other 224 00:12:03,680 --> 00:12:06,680 sites like that that already have Creative Commons repositories 225 00:12:06,680 --> 00:12:10,680 of strain names and like their parents listed, and then we're 226 00:12:10,680 --> 00:12:14,680 putting it in Gephi, which is a data visualizer that does the 227 00:12:14,680 --> 00:12:18,680 network graphs, and you can build like these genealogy networks. 228 00:12:18,680 --> 00:12:22,680 And so you can actually be like, okay, so all these strains are 229 00:12:22,680 --> 00:12:25,680 in this cluster, so they're not very diverse, at least by 230 00:12:25,680 --> 00:12:28,680 self-reported data, not always the most accurate, but then at 231 00:12:28,680 --> 00:12:30,680 least it's what we've got, right? 232 00:12:30,680 --> 00:12:34,680 And then we can take that and then be like, oh, but these 233 00:12:34,680 --> 00:12:37,680 strains over here are in like a list like what Keegan's going to 234 00:12:37,680 --> 00:12:42,680 go over, and oh, those ones are actually like very unique. 235 00:12:42,680 --> 00:12:45,680 They're not in this cluster or they're not in our galaxy at all. 236 00:12:45,680 --> 00:12:48,680 I think that's an interesting way to pursue that line of 237 00:12:48,680 --> 00:12:52,680 questioning. 238 00:12:52,680 --> 00:12:56,680 Ruth, you may want to go because I feel like there are so many 239 00:12:56,680 --> 00:12:58,680 good ideas here. 240 00:12:58,680 --> 00:13:02,680 Yeah, well, I just read an article and I posted a link to 241 00:13:02,680 --> 00:13:04,680 it in the comments. 242 00:13:04,680 --> 00:13:07,680 They discovered a new class in addition to terpenes called 243 00:13:07,680 --> 00:13:12,680 flavorants, which are adding, you know, supplementing and 244 00:13:12,680 --> 00:13:16,680 distinguishing the smells and they think the effects because 245 00:13:16,680 --> 00:13:19,680 they said if you look across strains, you have plants with 246 00:13:19,680 --> 00:13:22,680 very different, with similar terpenes are creating different 247 00:13:22,680 --> 00:13:24,680 effects. 248 00:13:24,680 --> 00:13:29,680 So this is yet another differentiator and we know that 249 00:13:29,680 --> 00:13:35,680 they're finding all new, I guess, markers. 250 00:13:35,680 --> 00:13:39,680 They're still trying to figure out how different compounds 251 00:13:39,680 --> 00:13:43,680 create their effects and what are the important ingredients. 252 00:13:43,680 --> 00:13:46,680 And, you know, first we start with cannabinoids and then we 253 00:13:46,680 --> 00:13:49,680 moved on to terpenes and I've heard of flavonoids, but I just 254 00:13:49,680 --> 00:13:53,680 heard about flavorants and it's just, I'll stop there. 255 00:13:53,680 --> 00:13:55,680 I'll go on forever if I don't. 256 00:13:55,680 --> 00:14:01,680 I found the first batch of COAs that had flavonoids on them. 257 00:14:01,680 --> 00:14:07,680 We've been collecting Florida COAs, haven't parsed them yet. 258 00:14:07,680 --> 00:14:14,680 We're moving so quick, it's hard to, right, that's why it's all 259 00:14:14,680 --> 00:14:17,680 hands on deck and I love having your help here because I only 260 00:14:17,680 --> 00:14:19,680 have so much time to even look at the data. 261 00:14:19,680 --> 00:14:22,680 So a lot of the data goes unlooked at. 262 00:14:22,680 --> 00:14:28,680 But we are collecting COAs from Florida and I have noticed that 263 00:14:28,680 --> 00:14:33,680 some of, not all, but some of the tests do have flavonoids on 264 00:14:33,680 --> 00:14:34,680 there. 265 00:14:34,680 --> 00:14:37,680 So it's going to be a whole other suite of compounds. 266 00:14:37,680 --> 00:14:41,680 We're going to have to think about how to collect and 267 00:14:41,680 --> 00:14:45,680 standardize. 268 00:14:45,680 --> 00:14:48,680 And then the other thought that goes in line with both Caleb and 269 00:14:48,680 --> 00:14:52,680 Yasha's thought is that's an even better idea than looking at 270 00:14:52,680 --> 00:14:56,680 the number of unique strains by month is somehow think of a 271 00:14:56,680 --> 00:15:03,680 metric that encapsulates the diversity of chemicals you're 272 00:15:03,680 --> 00:15:04,680 measuring. 273 00:15:04,680 --> 00:15:07,680 I don't even know what it would be, but I'm sure there would be 274 00:15:07,680 --> 00:15:13,680 some statistical, some statistic, which is basically, I don't 275 00:15:13,680 --> 00:15:15,680 know, variance or something. 276 00:15:15,680 --> 00:15:18,680 Maybe you just look at, but I don't know how you would do it 277 00:15:18,680 --> 00:15:20,680 cross compound. 278 00:15:20,680 --> 00:15:24,680 So maybe that's something for you all to kind of research. 279 00:15:24,680 --> 00:15:29,680 You know, what statistical concept could encapsulate just 280 00:15:29,680 --> 00:15:36,680 could we somehow measure just the total variation of chemicals 281 00:15:36,680 --> 00:15:39,680 we're seeing on a month by month basis? 282 00:15:39,680 --> 00:15:42,680 So that's a big concept. 283 00:15:42,680 --> 00:15:45,680 So that Yasha, that and Caleb, brilliant ideas. 284 00:15:45,680 --> 00:15:47,680 We'll have to investigate that further. 285 00:15:47,680 --> 00:15:49,680 So I don't think we can do that today. 286 00:15:49,680 --> 00:15:52,680 We may be able to off road with the strain names by month. 287 00:15:52,680 --> 00:15:58,680 So we'll try to save time to do that. 288 00:15:58,680 --> 00:16:01,680 And then actually final thought, and then Larissa and Candice, I 289 00:16:01,680 --> 00:16:03,680 don't want to leave you out of the picture, so I'll let you 290 00:16:03,680 --> 00:16:04,680 jump in. 291 00:16:04,680 --> 00:16:09,680 But final thought is, Caleb, what's cool is I'll get you a 292 00:16:09,680 --> 00:16:16,680 large data set today, but it's kind of lacking in data points. 293 00:16:16,680 --> 00:16:19,680 So the data set we have today, it's got like strain name, total 294 00:16:19,680 --> 00:16:24,680 THC, and that's pretty much it. 295 00:16:24,680 --> 00:16:26,680 And when it was tested. 296 00:16:26,680 --> 00:16:32,680 What you could do is what I'd love to do is augment data sets. 297 00:16:32,680 --> 00:16:38,680 You would take this data set and augment it with your own strain 298 00:16:38,680 --> 00:16:39,680 data. 299 00:16:39,680 --> 00:16:42,680 And it's going to be imperfect, right? 300 00:16:42,680 --> 00:16:47,680 You would love to actually have those samples tested for terpenes. 301 00:16:47,680 --> 00:16:49,680 But I don't know. 302 00:16:49,680 --> 00:16:56,680 I think any measure is better than no measure. 303 00:16:56,680 --> 00:17:00,680 And so basically what you're augmenting with it would basically be 304 00:17:00,680 --> 00:17:01,680 averages. 305 00:17:01,680 --> 00:17:07,680 So you, for example, we've got blue dream tested. 306 00:17:07,680 --> 00:17:11,680 So, you know, what if you augmented that with just the average 307 00:17:11,680 --> 00:17:17,680 terpene values you've seen for blue dream? 308 00:17:17,680 --> 00:17:19,680 So that's a great idea. 309 00:17:19,680 --> 00:17:20,680 Yeah. 310 00:17:20,680 --> 00:17:22,680 I know that I love your red ink. 311 00:17:22,680 --> 00:17:23,680 So jump in. 312 00:17:23,680 --> 00:17:24,680 Yeah. 313 00:17:24,680 --> 00:17:25,680 Sorry. 314 00:17:25,680 --> 00:17:26,680 There's so much to say about this. 315 00:17:26,680 --> 00:17:28,680 I think it's a fascinating project. 316 00:17:28,680 --> 00:17:33,680 I know that a huge number of flowers that are sold are not two 317 00:17:33,680 --> 00:17:36,680 flowers that are sold as blue dream may have nothing to do with each 318 00:17:36,680 --> 00:17:39,680 other genetically or through their compounds. 319 00:17:39,680 --> 00:17:44,680 But what I do believe we would see is a cluster of samples being 320 00:17:44,680 --> 00:17:47,680 nearly identical within blue dream. 321 00:17:47,680 --> 00:17:51,680 And maybe those can be identified as, okay, let's call this A1. 322 00:17:51,680 --> 00:17:54,680 And then many other clusters like that. 323 00:17:54,680 --> 00:17:56,680 Listen, I would talk about this all day. 324 00:17:56,680 --> 00:17:57,680 I'll stop. 325 00:17:57,680 --> 00:18:03,680 But there's one more thing that a single strain may have slightly or 326 00:18:03,680 --> 00:18:10,680 significantly different analytes based on its age and growing 327 00:18:10,680 --> 00:18:11,680 conditions. 328 00:18:11,680 --> 00:18:14,680 But I do believe that we likely have enough data through off the 329 00:18:14,680 --> 00:18:19,680 shelf testing and open data that would be able to show what is a 330 00:18:19,680 --> 00:18:26,680 single strain and how broad does it go within testing? 331 00:18:26,680 --> 00:18:28,680 Thank you. 332 00:18:28,680 --> 00:18:30,680 You're 100% correct. 333 00:18:30,680 --> 00:18:36,680 And basically what you're working on is there's going to be error, 334 00:18:36,680 --> 00:18:38,680 right, as soon as we estimate. 335 00:18:38,680 --> 00:18:42,680 I mean, as soon as we introduce estimates, there's going to be error. 336 00:18:42,680 --> 00:18:44,680 And the error may be non-negligible. 337 00:18:44,680 --> 00:18:46,680 There could be strong biases. 338 00:18:46,680 --> 00:18:48,680 There's a lot going on. 339 00:18:48,680 --> 00:18:55,680 So what I would hope is if you say you were going to do that, would 340 00:18:55,680 --> 00:19:00,680 love a large sample size because as we've mentioned, you know, the 341 00:19:00,680 --> 00:19:01,680 law of large numbers. 342 00:19:01,680 --> 00:19:06,680 But I don't know if that would necessarily get rid of any bias. 343 00:19:06,680 --> 00:19:08,680 So the bias may be baked in. 344 00:19:08,680 --> 00:19:12,680 But why would I even do that? 345 00:19:12,680 --> 00:19:17,680 Well, I would just want basically just a rough idea. 346 00:19:17,680 --> 00:19:21,680 So today we'll look at, like, say the top 20 strains in Michigan. 347 00:19:21,680 --> 00:19:27,680 And, you know, wouldn't it be cool to just know just approximately what 348 00:19:27,680 --> 00:19:34,680 is the chemical profile of these top strains? 349 00:19:34,680 --> 00:19:39,680 Unfortunately, we don't have the actual terpene data. 350 00:19:39,680 --> 00:19:42,680 It may not have been measured. 351 00:19:42,680 --> 00:19:46,680 Those products were grown, sold, smoked, gone. 352 00:19:46,680 --> 00:19:49,680 So we can't even measure them for terpenes anymore. 353 00:19:49,680 --> 00:19:53,680 So what do we do when there's missing data? 354 00:19:53,680 --> 00:19:55,680 The best we can do is estimate. 355 00:19:55,680 --> 00:20:01,680 But once again, the bias and error may be so great, it may be not 356 00:20:01,680 --> 00:20:03,680 worthwhile. 357 00:20:03,680 --> 00:20:06,680 So good considerations all around. 358 00:20:06,680 --> 00:20:08,680 But Caleb, you have some thoughts? 359 00:20:08,680 --> 00:20:12,680 And then Larisa and Candice, I'll let you chime in if you want to. 360 00:20:12,680 --> 00:20:17,680 Sorry to continue this rabbit hole, but I just thought maybe about the 361 00:20:17,680 --> 00:20:22,680 missing data that the consumers may also not have that information. 362 00:20:22,680 --> 00:20:26,680 And so the relevance for consumer choice may not be super high because 363 00:20:26,680 --> 00:20:31,680 they may be making the decision based on the perception of the strain, 364 00:20:31,680 --> 00:20:36,680 which is what we have the data of. 365 00:20:36,680 --> 00:20:41,680 That's a phenomenal point. 366 00:20:41,680 --> 00:20:48,680 I don't even know all the implications, but it's definitely a thing. 367 00:20:48,680 --> 00:20:53,680 People definitely say they could go and ask for a blue dream. 368 00:20:53,680 --> 00:20:59,680 That one's maybe a bit more standard, but something like a GMO, that could 369 00:20:59,680 --> 00:21:04,680 be all over the board, in my opinion. 370 00:21:04,680 --> 00:21:09,680 But anywho, those are just opinions. 371 00:21:09,680 --> 00:21:12,680 We can get to the data here in a second. 372 00:21:12,680 --> 00:21:17,680 But Larisa, you don't have to be put on the spot, but if you want to chime 373 00:21:17,680 --> 00:21:20,680 in and put anything on the table for today, you're welcome to. 374 00:21:20,680 --> 00:21:26,680 Basically we're talking about lab tests, cannabis data, and specifically I 375 00:21:26,680 --> 00:21:35,680 have a big data set from Michigan to share with you today. 376 00:21:35,680 --> 00:21:38,680 And you're also welcome to just listen in. 377 00:21:38,680 --> 00:21:42,680 So just interrupt forcefully if you want to at any point. 378 00:21:42,680 --> 00:21:47,680 But Candice, do you have anything you want to put on the table for today? 379 00:21:47,680 --> 00:21:48,680 I don't have anything. 380 00:21:48,680 --> 00:21:50,680 I think this is great, though. 381 00:21:50,680 --> 00:21:56,680 Being able to pick a product based on chemical profile versus somebody 382 00:21:56,680 --> 00:22:00,680 coming up with strain names would be a great future for patients and 383 00:22:00,680 --> 00:22:03,680 consumers. 384 00:22:03,680 --> 00:22:04,680 Yeah, exactly. 385 00:22:04,680 --> 00:22:09,680 And I think that it harks back to what's the point of all of this? 386 00:22:09,680 --> 00:22:11,680 Why are we on this whole rabbit? 387 00:22:11,680 --> 00:22:14,680 Why are we down this rabbit hole in the first place? 388 00:22:14,680 --> 00:22:19,680 Well, what data do we have? 389 00:22:19,680 --> 00:22:23,680 We have often, like we said, just the strain name. 390 00:22:23,680 --> 00:22:28,680 And if you go to the store, you're going to see strain names and the THC 391 00:22:28,680 --> 00:22:29,680 numbers. 392 00:22:29,680 --> 00:22:34,680 But as we've mentioned, what's more important and what we think is the 393 00:22:34,680 --> 00:22:39,680 actual causal effect, right, it's not that the product was named Blue 394 00:22:39,680 --> 00:22:43,680 Dream that it's having a certain effect, but it's actually the chemicals 395 00:22:43,680 --> 00:22:44,680 you're consuming. 396 00:22:44,680 --> 00:22:49,680 So that's basically what we're after. 397 00:22:49,680 --> 00:22:56,680 And as always, we think from my economics training or schooling that 398 00:22:56,680 --> 00:23:03,680 the better information consumers have, then they'll be able to make better 399 00:23:03,680 --> 00:23:04,680 decisions. 400 00:23:04,680 --> 00:23:13,680 And on average, they'll live happier lives. 401 00:23:13,680 --> 00:23:14,680 So that's all. 402 00:23:14,680 --> 00:23:19,680 And so that's why Copy Left Cultivores, Caleb, you're doing a really good 403 00:23:19,680 --> 00:23:24,680 deed is by simply getting that information out there. 404 00:23:24,680 --> 00:23:27,680 You should be helping people make better decisions, right? 405 00:23:27,680 --> 00:23:32,680 Not everybody is going to look at the data, but the ones that are interested, 406 00:23:32,680 --> 00:23:37,680 they should be able to look at the data if they want to. 407 00:23:37,680 --> 00:23:46,680 But I didn't do the best job saying the mission, but hopefully you all have 408 00:23:46,680 --> 00:23:50,680 your own missions as well, and this is in accordance with them. 409 00:23:50,680 --> 00:23:57,680 But on that note, instead of me just being long-winded and speaking, do you 410 00:23:57,680 --> 00:24:00,680 want to actually see the data we have here? 411 00:24:00,680 --> 00:24:03,680 So that could be quite fun. 412 00:24:03,680 --> 00:24:07,680 So let me share my screen with you. 413 00:24:07,680 --> 00:24:11,680 So instead of just, right, that's what's fun here is instead of just talk, 414 00:24:11,680 --> 00:24:15,680 talk, talk, we can actually look at the data. 415 00:24:15,680 --> 00:24:25,680 So last week, we were fortunate enough to be able to see the data from 416 00:24:25,680 --> 00:24:26,680 Massachusetts. 417 00:24:26,680 --> 00:24:34,680 And today we're fortunate to be able to see the total THC numbers for these 418 00:24:34,680 --> 00:24:38,680 products in Michigan. 419 00:24:38,680 --> 00:24:45,680 And we've got a time range here dating from, here I'll print it out here in 420 00:24:45,680 --> 00:24:47,680 one second. 421 00:24:47,680 --> 00:24:57,680 It's roughly the third quarter of 2020 through the end of 2022. 422 00:24:57,680 --> 00:25:03,680 Don't have a ton of data, but we basically have an indicator if it's 423 00:25:03,680 --> 00:25:05,680 medical or adult use. 424 00:25:05,680 --> 00:25:09,680 We know which lab tested it. 425 00:25:09,680 --> 00:25:12,680 Everything I believe is marked. 426 00:25:12,680 --> 00:25:15,680 Actually, there's various types of categories. 427 00:25:15,680 --> 00:25:21,680 And so this is in fact a place for further data cleaning if any of you all 428 00:25:21,680 --> 00:25:23,680 are interested. 429 00:25:23,680 --> 00:25:25,680 But... 430 00:25:25,680 --> 00:25:27,680 I apologize, do you mind if I interrupt really quick? 431 00:25:27,680 --> 00:25:29,680 Please. 432 00:25:29,680 --> 00:25:31,680 So two quick things. 433 00:25:31,680 --> 00:25:33,680 I actually have to jump off in a minute. 434 00:25:33,680 --> 00:25:35,680 I will be looking for this video later. 435 00:25:35,680 --> 00:25:44,680 And just so you're aware, total THC in Michigan is defined as.877 times THCA 436 00:25:44,680 --> 00:25:48,680 plus delta 9THC plus delta 8THC. 437 00:25:48,680 --> 00:25:51,680 Just so that when you compare it to other states, that's how... 438 00:25:51,680 --> 00:25:53,680 Can you say that one more time, please? 439 00:25:53,680 --> 00:25:59,680 So it's decarboxylated THCA plus delta 9THC plus delta 8THC. 440 00:25:59,680 --> 00:26:02,680 Okay, so it also has delta 8THC. 441 00:26:02,680 --> 00:26:04,680 But that's so rare that it shouldn't make any difference. 442 00:26:04,680 --> 00:26:10,680 Yeah, from my understanding, that one was usually less than.1% when we 443 00:26:10,680 --> 00:26:12,680 observed it. 444 00:26:12,680 --> 00:26:15,680 So that will buys the numbers up. 445 00:26:15,680 --> 00:26:19,680 But just a hair. 446 00:26:19,680 --> 00:26:21,680 But that's a really good note. 447 00:26:21,680 --> 00:26:29,680 And that's why as a data scientist, it's worthwhile to wear many caps. 448 00:26:29,680 --> 00:26:33,680 And one of the caps you want to wear is historian slash... 449 00:26:33,680 --> 00:26:35,680 Well, you are a researcher. 450 00:26:35,680 --> 00:26:40,680 But you want to be digging through all the testing regulations and figure out 451 00:26:40,680 --> 00:26:44,680 things like that because they can have big implications. 452 00:26:44,680 --> 00:26:46,680 But good find, Yasha. 453 00:26:46,680 --> 00:26:48,680 But I'll move quick here. 454 00:26:48,680 --> 00:26:54,680 But long story short, we've got data from the end of June 2020. 455 00:26:54,680 --> 00:27:00,680 And then the last observation was the beginning of 2023. 456 00:27:00,680 --> 00:27:04,680 So I'll start limiting the time range further along. 457 00:27:04,680 --> 00:27:11,680 But real quick, really just going to focus on, of course, total THC today. 458 00:27:11,680 --> 00:27:15,680 So I'll leave all the cool terpenes to Caleb. 459 00:27:15,680 --> 00:27:20,680 But what can we do with this limited number of data points? 460 00:27:20,680 --> 00:27:24,680 So once again, I think we can go pretty far with this. 461 00:27:24,680 --> 00:27:27,680 So what do we have here? 462 00:27:27,680 --> 00:27:34,680 We had 80,000 samples that were tested in this time range. 463 00:27:34,680 --> 00:27:38,680 And let's just start visualizing them. 464 00:27:38,680 --> 00:27:41,680 So just have tons of visualizations for you today. 465 00:27:41,680 --> 00:27:45,680 So we'll just start going through them one by one. 466 00:27:45,680 --> 00:27:53,680 Whatever reason, the plotting doesn't take my style right away. 467 00:27:53,680 --> 00:28:03,680 So here's where we were in 2020 with about a thousand tests per month. 468 00:28:03,680 --> 00:28:12,680 And you notice while it has been turbulent times in the cannabis industry in Michigan, 469 00:28:12,680 --> 00:28:21,680 Michigan has seen what I would say is substantial growth over the past two and a half years. 470 00:28:21,680 --> 00:28:25,680 And we still need to know what's going on this year. 471 00:28:25,680 --> 00:28:27,680 It's 2023. 472 00:28:27,680 --> 00:28:30,680 But we'll see that data when the time comes. 473 00:28:30,680 --> 00:28:34,680 But this is what you want to see in a market. 474 00:28:34,680 --> 00:28:37,680 This is, that's growth. 475 00:28:37,680 --> 00:28:45,680 If you're a lab in Michigan, that's a lot of revenue for you. 476 00:28:45,680 --> 00:28:56,680 And then as always, we say that the number of tests is essentially a proxy for the supply. 477 00:28:56,680 --> 00:29:04,680 Once again, it's an imperfect estimate because batch sizes may be changing. 478 00:29:04,680 --> 00:29:10,680 Maybe they're still producing the same amount of cannabis, but they're just making it in smaller batches. 479 00:29:10,680 --> 00:29:20,680 But if you go under the assumption that batch size is staying the same during this time period, 480 00:29:20,680 --> 00:29:26,680 then you would think, OK, tests are growing. 481 00:29:26,680 --> 00:29:36,680 Ruth had mentioned medical cannabis, and we can actually break this into two pieces here. 482 00:29:36,680 --> 00:29:40,680 The adult use versus the medical. 483 00:29:40,680 --> 00:29:47,680 And I'll let you all make your own interpretations of this. 484 00:29:47,680 --> 00:30:00,680 But what I interpret this is, right, they're both around 500 tests per month in the beginning. 485 00:30:00,680 --> 00:30:11,680 And I think maybe the medical and adult use, right, combined were just filling the entire supply of Michigan. 486 00:30:11,680 --> 00:30:15,680 And maybe there was kind of some overlap. 487 00:30:15,680 --> 00:30:19,680 Maybe some adult use consumers were getting medical. 488 00:30:19,680 --> 00:30:25,680 Maybe some medical consumers were getting adult use, maybe a little bit of crossover. 489 00:30:25,680 --> 00:30:36,680 And so I think what you see is you see the rise of adult, the rise in the market, and that's mostly going to adult use. 490 00:30:36,680 --> 00:30:48,680 And it looks to me like maybe the people that there were some people buying medical, and they may have transitioned to buying adult use. 491 00:30:48,680 --> 00:31:01,680 But it looks like there's, you know, may kind of reach almost like a steady state there of, you know, around 500 or so medical tests per month. 492 00:31:01,680 --> 00:31:10,680 So that's kind of my interpretation of this is there is basically like a baseline medical community. 493 00:31:10,680 --> 00:31:24,680 But maybe, you know, in the early days, you know, maybe some people were seeking adult use, but they were classified as medical. 494 00:31:24,680 --> 00:31:31,680 So that's my interpretation of it. Do any of you want to chime in on your thoughts? 495 00:31:31,680 --> 00:31:43,680 Yeah, I. Yeah, you see this exact same pattern in every state that opens up. And this is a little misleading because in every state you have medical comes first. 496 00:31:43,680 --> 00:31:50,680 Medical predates for usually a couple of years and medical use takes off. 497 00:31:50,680 --> 00:31:58,680 And then as soon as rec use comes in, medical use starts to level off and then it declines and it essentially dies. 498 00:31:58,680 --> 00:32:07,680 And the adult use ends up essentially killing off medical because all the focus and resource resources switch away from medical. 499 00:32:07,680 --> 00:32:14,680 And it's very unfortunate. But it's I mean, it's the reality. 500 00:32:14,680 --> 00:32:19,680 And what's interesting here is I have exact same graphs on sales and it looks identical. 501 00:32:19,680 --> 00:32:30,680 So in this case, yes, the tests are very good proxy for total revenues. But again, you see this exact same picture in every state. 502 00:32:30,680 --> 00:32:37,680 Good, good point. I would just like to say, you know, I wouldn't count the medical market out yet. 503 00:32:37,680 --> 00:32:45,680 You know, it's maybe still about, you know, a fifth to a quarter the size of the adult use market. 504 00:32:45,680 --> 00:32:52,680 So anything that large is non negligible. And this is actually I guess more than 500. 505 00:32:52,680 --> 00:33:03,680 It's between 500 and a thousand. But Ruth, unfortunately, I think you do speak some truth in that you're right. 506 00:33:03,680 --> 00:33:15,680 If, say, you did get into business to produce medical cannabis, you know, that would be, you know, taking taking a hit. 507 00:33:15,680 --> 00:33:31,680 But. But yes, I went I'm going to have to research more about the licensing, because I wonder, is it possible for a license to produce both medical and adult use cannabis? 508 00:33:31,680 --> 00:33:39,680 Or do you have to produce specifically for one type that varies by state and the laws have changed over time. 509 00:33:39,680 --> 00:33:47,680 And early on, they said to growers, you have to you have to designate which plants are medical and which are recreational. 510 00:33:47,680 --> 00:33:53,680 And they kept them separate throughout the pipeline. So the supply chain. But I think there's intermingling. 511 00:33:53,680 --> 00:34:04,680 You also have differences by state with big implications. And for example, in California, what you see is the people involved in medical tend to do everything medical. 512 00:34:04,680 --> 00:34:09,680 And they're very different companies in organizations from people involved in rec. 513 00:34:09,680 --> 00:34:21,680 However, in other states, most other states, as I said, you start off with medical and the first licensees for medical and then the medical licensees get priority when they open up to adult use. 514 00:34:21,680 --> 00:34:27,680 So the medical licensees are also adult use growers and dispensers. 515 00:34:27,680 --> 00:34:37,680 And this is kind of creating a problem because a new state, you have a lot of organizations go in there and the money is in adult use and they don't really care about medical. 516 00:34:37,680 --> 00:34:42,680 But they go in at medical in order to get the priority for when they open up to adult use. 517 00:34:42,680 --> 00:34:45,680 There's a huge amount of gaming going on. 518 00:34:45,680 --> 00:34:57,680 But there's there's huge implications for, as I said, for resources available for medical users and adult use users and essentially all the money is in adult use. 519 00:34:57,680 --> 00:35:02,680 So the resources very quickly get focused and shift over to adult use. 520 00:35:02,680 --> 00:35:21,680 And one last thing is I believe that when they did the Booker Schumer Booker Schumer Weiman proposal or whatever, this was kind of an iteration back on what the national bill for for for legalizing cannabis federally looked like. 521 00:35:21,680 --> 00:35:28,680 My understanding is that whole bill, however long it was, didn't include the word patient once. 522 00:35:28,680 --> 00:35:32,680 So even federal government isn't really focused on that. 523 00:35:32,680 --> 00:35:41,680 And my belief is that all medical use is going to end up being pushed into the pharma industry and the pharma model, which personally I'm no fan of. 524 00:35:41,680 --> 00:35:44,680 But I think that's where it's going. 525 00:35:44,680 --> 00:35:52,680 It's definitely something that's going to have to be addressed at some point, Ruth, because, as you pointed out, this has big implications. 526 00:35:52,680 --> 00:36:00,680 Because you're kind of like yo yoing around these medical patients and consumers. 527 00:36:00,680 --> 00:36:16,680 The problem is that if you look at and this would be a very interesting analysis is if you go and if you look at all the COAs that are labeled for medical and that are labeled for adult use and you compare them, the adult the adult use is focused on high THC cannabis. 528 00:36:16,680 --> 00:36:25,680 But the medical have much more variety and there's much lower THC and there's higher ratios of CBD to THC. 529 00:36:25,680 --> 00:36:36,680 And there's just and so the problem is, is when you get pushed over to adult use, you lose all that variety that the medical users need in order to address a particular conditions. 530 00:36:36,680 --> 00:36:46,680 Actually, let's go off-roading because Ruth, you just raised the most one of the most critical, one of the most interesting things. 531 00:36:46,680 --> 00:36:58,680 So I'll have to maybe circle back for some of this analysis on on labs for for Yasha's sake because I'm sure Yasha will find this interesting. 532 00:36:58,680 --> 00:37:02,680 But actually, Ruth, we can once again. 533 00:37:02,680 --> 00:37:07,680 It's just a one hour meetup after all, so we can only get so far. 534 00:37:07,680 --> 00:37:10,680 So, you know, don't take my analysis as gospel. 535 00:37:10,680 --> 00:37:14,680 But what we can do is. 536 00:37:14,680 --> 00:37:22,680 Chalk out how you could actually begin to analyze that because remember, we've got strain data here. 537 00:37:22,680 --> 00:37:29,680 We have total THC here and we have medical classification. 538 00:37:29,680 --> 00:37:35,680 So we can see. 539 00:37:35,680 --> 00:37:42,680 Are there, you know, more or less medical strains, maybe relative to the number of tests. 540 00:37:42,680 --> 00:37:46,680 So we talked about diversity in strains. 541 00:37:46,680 --> 00:37:49,680 Well, it could vary between medical and adult use. 542 00:37:49,680 --> 00:37:54,680 And then, too, we can check out the total THC numbers. 543 00:37:54,680 --> 00:37:57,680 Actually, let's do that right now. 544 00:37:57,680 --> 00:38:09,680 So so first, before we get into the conditionals, here's just the unconditional average THC. 545 00:38:09,680 --> 00:38:16,680 And remember, Yasha's point that this includes Delta eight to. 546 00:38:16,680 --> 00:38:27,680 OK, so let's put the medical on the side burner for now, but this is we'll do some off roading because it's more fun anyways. 547 00:38:27,680 --> 00:38:30,680 But here's the total THC. Cool. 548 00:38:30,680 --> 00:38:33,680 We've seen that distribution before. 549 00:38:33,680 --> 00:38:42,680 And just the only note is the 99th percentile is actually similar to what we observed in Massachusetts. 550 00:38:42,680 --> 00:38:48,680 The mean actually may be similar to but maybe slightly higher. 551 00:38:48,680 --> 00:38:55,680 And actually, this is just the. 552 00:38:55,680 --> 00:39:07,680 When the first condition we've added, but here's the distributions between medical THC and adult use THC. 553 00:39:07,680 --> 00:39:17,680 And if you look at it's actually it's actually kind of interesting if we look at year by year. 554 00:39:17,680 --> 00:39:20,680 But this this one is for twenty twenty two. 555 00:39:20,680 --> 00:39:30,680 And so you can actually do a test to see if they're statistically different. 556 00:39:30,680 --> 00:39:40,680 But I think it's just that we have just a giant sample size, so it's maybe easy to see the conclude that things are different. 557 00:39:40,680 --> 00:39:48,680 But it could be that the difference may not actually be that much. 558 00:39:48,680 --> 00:39:55,680 Can you get it? Can you get a count? And also, can you do CBD instead of THC? 559 00:39:55,680 --> 00:40:02,680 We don't actually have CBD here, unfortunately. 560 00:40:02,680 --> 00:40:05,680 I wonder if we could do that one in Washington. 561 00:40:05,680 --> 00:40:10,680 But here I'll get you the count for them real quick. 562 00:40:10,680 --> 00:40:23,680 I think we have ten thousand medical samples and thirty three thousand adult use samples in twenty twenty two. 563 00:40:23,680 --> 00:40:26,680 That's a decent percentage for the medical. 564 00:40:26,680 --> 00:40:30,680 So I'm surprised that they're that close. 565 00:40:30,680 --> 00:40:33,680 Well, Michigan's are super interesting market. 566 00:40:33,680 --> 00:40:39,680 Michigan's went a mark to this phrase gets used a lot these days that people are sleeping on something. 567 00:40:39,680 --> 00:40:48,680 Well, people were sleeping on Michigan in the sense that it's not your traditional East Coast market. 568 00:40:48,680 --> 00:40:52,680 And it's not your traditional West Coast market either. 569 00:40:52,680 --> 00:41:02,680 Like the the West Coast markets like California, Oregon, Washington and Colorado. 570 00:41:02,680 --> 00:41:09,680 I mean, I think those markets are quite mature at this stage. 571 00:41:09,680 --> 00:41:19,680 And you kind of saw like some pretty like like there are a lot of companies there, but there's definitely some big, big players there. 572 00:41:19,680 --> 00:41:27,680 And then the East Coast is way as much different because the story there is just regulations. 573 00:41:27,680 --> 00:41:34,680 Right. Like from my understanding, really competitive, maybe to get a license like maybe in Massachusetts. 574 00:41:34,680 --> 00:41:38,680 Super difficult to get a license in Florida. 575 00:41:38,680 --> 00:41:48,680 And then, you know, the difference between the left coast and the right coast, the east and west, the West Coast is generally unintegrated. 576 00:41:48,680 --> 00:41:51,680 You're allowed to integrate, but you're not required. 577 00:41:51,680 --> 00:41:56,680 And in almost every case on the East Coast, you're required to be vertically integrated. 578 00:41:56,680 --> 00:42:07,680 So on the on the West in Colorado, California, Oregon, Washington, Michigan, you have hundreds or thousands of licensees. 579 00:42:07,680 --> 00:42:17,680 Whereas in the East, you only have tens of licensees because you you end up with a few large companies because they're they're required to be vertically integrated. 580 00:42:17,680 --> 00:42:23,680 And that creates a hugely different dynamic in all respects. 581 00:42:23,680 --> 00:42:30,680 And exactly. And then so Michigan just didn't fit either of those models. 582 00:42:30,680 --> 00:42:40,680 In fact, its history goes back to I think they originally there was a lot of small time growers who were maybe were doing mostly medical. 583 00:42:40,680 --> 00:42:49,680 From my understanding, there was like just a lot of medical plots, you know, like, you know, the hundred hundred plants or what have you. 584 00:42:49,680 --> 00:42:59,680 So I think. I think your medical thing was there first in Michigan, and it's had a long history. 585 00:42:59,680 --> 00:43:02,680 So it's not like people there are inexperienced. 586 00:43:02,680 --> 00:43:12,680 So you have a bunch of highly experienced growers that were starting at a lot of small businesses. 587 00:43:12,680 --> 00:43:21,680 And actually now, now is a good time to show this to like, for example, like, look at the number of labs in Michigan. 588 00:43:21,680 --> 00:43:32,680 Like, there's there's 24 labs in Michigan, and it's just not really, of course, like they're dominated by a couple of them. 589 00:43:32,680 --> 00:43:42,680 But, you know, like I think in Washington, we're down, you know, there's maybe fewer than 10 labs now. 590 00:43:42,680 --> 00:43:52,680 So it's like I said, me included, I was just sleeping on Michigan. They just. 591 00:43:52,680 --> 00:43:59,680 It's a I mean, we saw it earlier with the the this chart here with the rise in adult use. 592 00:43:59,680 --> 00:44:05,680 It's a pretty it's a pretty vibrant market there. 593 00:44:05,680 --> 00:44:17,680 And for medical, too. So it's a long story short. Don't don't discount the market in Michigan. 594 00:44:17,680 --> 00:44:23,680 OK, so where were we though? Oh, yeah, there is. 595 00:44:23,680 --> 00:44:30,680 Let's get back to the strains instead of just talking so highly about Michigan. 596 00:44:30,680 --> 00:44:39,680 OK, so we're looking at the THC. We noticed that there may or may not be a difference between THC with adult use in medical. 597 00:44:39,680 --> 00:44:48,680 And my hypothesis there is maybe still either the overlap or here is actually my better my better hypothesis. 598 00:44:48,680 --> 00:44:54,680 The labs don't know how to handle medical. Right. 599 00:44:54,680 --> 00:45:03,680 So it's you know, they're they're basically just the labs, I think, are just testing maybe everything the same. 600 00:45:03,680 --> 00:45:08,680 But actually, that's not a really good, good hypothesis. 601 00:45:08,680 --> 00:45:15,680 I just realized because the medical growers would maybe select lower THC varieties. 602 00:45:15,680 --> 00:45:28,680 But OK, I don't know. That wasn't a very good idea. So maybe I'll move on to the next chart here unless anyone else has any thoughts, comments, questions. 603 00:45:28,680 --> 00:45:36,680 But here's here's just one chart that would just be pertinent to show once again. 604 00:45:36,680 --> 00:45:45,680 This is this is something that's come up a lot in prior meetups, so I don't want to spend too, too much time on it other than then pointed out. 605 00:45:45,680 --> 00:45:48,680 And then I kind of want to move on to the strains. 606 00:45:48,680 --> 00:45:56,680 But just, you know, here's just the average THC in Michigan around the 21 percent. 607 00:45:56,680 --> 00:46:06,680 And you see some labs testing higher than average and some labs actually testing much lower than average. 608 00:46:06,680 --> 00:46:11,680 And I actually is left for there's left for. 609 00:46:11,680 --> 00:46:18,680 So basically, I just wanted to make one comment here is basically if you look at this chart here. 610 00:46:18,680 --> 00:46:26,680 So keep in mind. So basically look at these two charts side by side. 611 00:46:26,680 --> 00:46:34,680 So you've got the average THC by lab and then you have the total number of lab tests. 612 00:46:34,680 --> 00:46:48,680 And so what you see here is. You know, lab lab one and lab nine, they do have large market shares, you know, and they are testing quite above average. 613 00:46:48,680 --> 00:47:01,680 But what's interesting is lab for you know, lab for has a large market share in their testing, you know, just about average. 614 00:47:01,680 --> 00:47:16,680 So basically, my comment there is there may be just a lot of cultivators out there who just truly want a good measure of their products. 615 00:47:16,680 --> 00:47:21,680 You know, they're just like, OK, like, you know, enough is enough of the high THC numbers. 616 00:47:21,680 --> 00:47:26,680 I actually just want like a good measure of what I'm growing. 617 00:47:26,680 --> 00:47:34,680 So so I just kind of wanted to point that out that this one lab has pretty much. 618 00:47:34,680 --> 00:47:40,680 Like their average pretty much matches the average in the market. 619 00:47:40,680 --> 00:47:44,680 And remember what we were saying about sort of the law of large numbers. 620 00:47:44,680 --> 00:47:50,680 And this is like the rule about the consensus is let a bunch of people measure something. 621 00:47:50,680 --> 00:47:56,680 And then the average is kind of close to the true average. 622 00:47:56,680 --> 00:48:11,680 So so basically, in my opinion, you know, the closer you are to this red line, which could be biased, then, you know, the closer you are to being accurate. 623 00:48:11,680 --> 00:48:22,680 And so I don't know, just wanted to to hark on a good job for lab for for being close to accurate and have a high market share. 624 00:48:22,680 --> 00:48:37,680 So, anywho, back to the strains, though, because that's sort of the more fun topic, because both Caleb and Ruth, you had pertinent questions and statistics that you're after there. 625 00:48:37,680 --> 00:48:42,680 OK, Caleb, this is where. 626 00:48:42,680 --> 00:48:51,680 I asked chat GPT to write me a quick function, but this is just to get the job done for today. 627 00:48:51,680 --> 00:48:55,680 This is in no way a substitute for what you're doing. 628 00:48:55,680 --> 00:49:04,680 So basically what the problem here is, is if we look at the product name. 629 00:49:04,680 --> 00:49:18,680 It is wild. You know, you've got Buds by Sensei Star, you know, triangle, mints, Buds. 630 00:49:18,680 --> 00:49:27,680 This one's got underscores in it. 100 GMO, crash or bulk flower. 631 00:49:27,680 --> 00:49:44,680 Ideally, we could identify the strain name out of this. Right. This is GMO, crash or probably, you know, this one's probably pineapple express. 632 00:49:44,680 --> 00:49:56,680 So on and so forth. And it looks like I even made a mistake here because I think our kid archive is on our list, but it looks like this one's actually Mac Bang. 633 00:49:56,680 --> 00:50:08,680 So long story short is just getting the strain name out of the product name is a non-trivial task. 634 00:50:08,680 --> 00:50:27,680 And so this function will imperfectly do work for today, but we've we've had whole meetups in the past talking about how how in the world are we going to get these strain names? 635 00:50:27,680 --> 00:50:43,680 Because, Caleb, that's kind of the heart of this is right. You're sourcing like known strain names, but there could be unknown strains that are out there that you can identify. 636 00:50:43,680 --> 00:50:57,680 But you know, you may have to identify them from data like this, which is, you know, an absolute mess. You know, what if nobody's documented GMO skittles before? 637 00:50:57,680 --> 00:51:08,680 Like, why would we want to leave that undocumented just because it's got a little bit of noise in the product name? 638 00:51:08,680 --> 00:51:22,680 So you all move quick since we're really have so much time. OK, so long story short, going to just add the the strain name and it's going to be imperfect here. 639 00:51:22,680 --> 00:51:38,680 But you can see that this is at least better than it was. So dinosaur food. That's a cool one. Never heard of that strain before. Oreos, Groundhog Day. 640 00:51:38,680 --> 00:51:53,680 So so this is just a way that we can start accumulating strain names because that's a challenge we've had is just finding like an original way to get strain names. 641 00:51:53,680 --> 00:51:59,680 But why why are we even doing this? Well, check this out. 642 00:51:59,680 --> 00:52:08,680 Once again, this is going to be imperfect, but you can start to just count the number of strains. 643 00:52:08,680 --> 00:52:15,680 And these are some of the classic strains that we see over and over again. Right. 644 00:52:15,680 --> 00:52:21,680 Wedding cake. Just I don't know what it is about it. 645 00:52:21,680 --> 00:52:29,680 I think it must just be really easy to grow, but it must also be a popular seller. 646 00:52:29,680 --> 00:52:35,680 So, Caleb, this is why I was saying that if you were able to augment your terpene data. 647 00:52:35,680 --> 00:52:46,680 Like that's what you could try to explain. Like, why are these the top strains in Michigan? 648 00:52:46,680 --> 00:52:55,680 Once again, as in like I'm going to use strains in quotation marks because, you know, what you know, what is a strain, so to speak. 649 00:52:55,680 --> 00:53:01,680 Great. See, here's the archive, which I think is actually the Mac. 650 00:53:01,680 --> 00:53:04,680 So long story short. 651 00:53:04,680 --> 00:53:18,680 This is what's growing. And here's where we can get really cool with it and maybe even try to go off road real quick. 652 00:53:18,680 --> 00:53:21,680 Bye. 653 00:53:21,680 --> 00:53:29,680 Here, in fact, while I'm talking about this chart, I'll show you basically how I code these days. 654 00:53:29,680 --> 00:53:35,680 So basically how I code these days is right. I've got it to do here. 655 00:53:35,680 --> 00:53:41,680 We want to look at the, you know, adult use versus medical top strains. 656 00:53:41,680 --> 00:53:49,680 So, you know, theoretically, I could just don't let me bore you to death with this. 657 00:53:49,680 --> 00:54:11,680 But basically, I just go to chat GPT and I would just say something like, can you visualize the top or the average brutal THC by top strains for medical products versus adult. 658 00:54:11,680 --> 00:54:16,680 Let you do medical products for now. 659 00:54:16,680 --> 00:54:24,680 So chat GPT will be working on this one, and then we'll see how close they how close they get. 660 00:54:24,680 --> 00:54:26,680 So long story short. 661 00:54:26,680 --> 00:54:34,680 Here are, you know, the top strains in Michigan. 662 00:54:34,680 --> 00:54:42,680 And the one thing I would like to point out to you is. 663 00:54:42,680 --> 00:54:49,680 You do see some strains of course, you know, testing well above average. 664 00:54:49,680 --> 00:54:55,680 Right. So, you know, cush mints GMO. 665 00:54:55,680 --> 00:55:02,680 Even wedding cake, you know, they're, they're probably significantly above average. 666 00:55:02,680 --> 00:55:18,680 But what's, what's cool is you actually see some here like like remember we talked about blue dream earlier, you do see some strains that are, I mean blue dream is significantly. 667 00:55:18,680 --> 00:55:22,680 Probably statistically below average. 668 00:55:22,680 --> 00:55:38,680 But, you know, it's a good two and a half percentage points, actually maybe even maybe even maybe couldn't be close to 3%. Oh yeah, so the average is about 21 and a half percent. 669 00:55:38,680 --> 00:55:53,680 So it's almost 4% lower in average total THC on average than the other strains and an orange creamsicle pure Michigan. 670 00:55:53,680 --> 00:56:01,680 And this one Franklin fields are also all, you know, a good bit below average. 671 00:56:01,680 --> 00:56:10,680 So, this is maybe an interesting signal that 672 00:56:10,680 --> 00:56:29,680 total THC may not be all there is to the picture. Right. If it was that no one would ever pick blue dream. Right. Blue dream made it on to the top 20, despite having a below average total THC. 673 00:56:29,680 --> 00:56:41,680 So this is Caleb, this is an opportunity for you is why is blue dream a top seller. 674 00:56:41,680 --> 00:56:50,680 People would love to know why, you know, why is pure Michigan, a top seller. 675 00:56:50,680 --> 00:57:01,680 This is what people are trying to figure out right everybody's getting plants in the ground. They want to know what plants are going to be successful. 676 00:57:01,680 --> 00:57:08,680 You know and, you know, consumers to rate. 677 00:57:08,680 --> 00:57:13,680 You know, it's fun to know what all the different strains there are in the market. 678 00:57:13,680 --> 00:57:22,680 Okay, so real quick. Let's just see real quick if Chachi PT was able to make any 679 00:57:22,680 --> 00:57:31,680 wasn't a they weren't able to do it for us. So let's see if real quick, if you have just a quick five minutes to add. 680 00:57:31,680 --> 00:57:46,680 I promised we'd go off road. So let's see if we can't find the top 20 medical strains and their average THC. 681 00:57:46,680 --> 00:58:03,680 Okay, so we're going to need to do something like this. But first, we want to make sure that we're only getting medical products. 682 00:58:03,680 --> 00:58:15,680 So, so, so let's just try this. So, and once again, when you go off road, you know, we may spin our tires, but it may work out. 683 00:58:15,680 --> 00:58:21,680 So let's see medical. 684 00:58:21,680 --> 00:58:28,680 We'll just call this medical. So let's just see if this works. 685 00:58:28,680 --> 00:58:36,680 Okay, so we have 8000 strains. Let's just take a look at this and just see what's going on with this. 686 00:58:36,680 --> 00:58:40,680 Okay, so we've got products here. 687 00:58:40,680 --> 00:58:52,680 Got the strain name. Okay, we should just be able to more or less just copy and paste the, the rest of this code. 688 00:58:52,680 --> 00:59:04,680 And then we can, you know, change it to medical, and then this should more or less work. And we did it without Chachi PT. Okay, so this is medical here. 689 00:59:04,680 --> 00:59:10,680 And then basically, I'm just replacing some sample with medical. 690 00:59:10,680 --> 00:59:21,680 And then that should do the trick. So over and then I'll just change some of the titles here. So overall medical, medical, 691 00:59:21,680 --> 00:59:35,680 overall 99 percentile medical THC, average to the top 20 medical strains. 692 00:59:35,680 --> 00:59:54,680 And then medical, the top 20 medical. Okay, and there we have it. We've hopefully answered a brand new question here. 693 00:59:54,680 --> 01:00:05,680 Oh, no. 694 01:00:05,680 --> 01:00:17,680 The strain counts as this last thing I need. 695 01:00:17,680 --> 01:00:24,680 Okay, one second. We may be able to do this. 696 01:00:24,680 --> 01:00:30,680 I would love to be able to do this for you. Sorry if I'm boring you. 697 01:00:30,680 --> 01:00:33,680 Cross our fingers. 698 01:00:33,680 --> 01:00:46,680 And we now know the top 20. Look, I misspelled it. So we'll just bear with that for the time being. 699 01:00:46,680 --> 01:00:55,680 So check this out. Here are the top 20 medical strains. 700 01:00:55,680 --> 01:01:00,680 And check this out. 701 01:01:00,680 --> 01:01:25,680 Here, Ruth, you may be interested in this in Caleb to walk, you know, what could potentially explain the difference between the why medical patients are favoring certain strains over why adult use patients are favoring certain strains. 702 01:01:25,680 --> 01:01:34,680 You see wedding cake and gorilla glue still high on the chart. 703 01:01:34,680 --> 01:01:42,680 Orange creamsicle has gone way up. Blue Dream has moved its rank up. 704 01:01:42,680 --> 01:01:58,680 And you see this, these two other strains that are below average THC Russian snow and batched, which I've got a feeling I botched that strain name. 705 01:01:58,680 --> 01:02:09,680 So that's, that's wild. And then check this out. You also see these two strains. The kush mints was on the top 20 before. 706 01:02:09,680 --> 01:02:27,680 But check this one out. You now have the S, the S the, what's it called SFV OG, the San Fernando Valley OG testing at a whopping 27% total THC. 707 01:02:27,680 --> 01:02:33,680 So that's, that's wild right there. 708 01:02:33,680 --> 01:02:53,680 But, but I, I may have gone a little over and want to be respectful of all of everybody's time. So I may go ahead and stop presenting, but we'd love to, to get any of your thoughts there. 709 01:02:53,680 --> 01:03:06,680 Did you find this analysis interesting? Was there anything that comes to mind? Do you see any fruitful avenues for further research? Love to get your input real quick. 710 01:03:06,680 --> 01:03:15,680 If anyone wants to chime in. 711 01:03:15,680 --> 01:03:31,680 You're looking at THC and I agree that's important, but there's other aspects there too. And so I think that if you were to say, well, why are these so popular? You need to dig past THC and that would probably explain it. 712 01:03:31,680 --> 01:03:35,680 Help explain it. 713 01:03:35,680 --> 01:03:48,680 Yeah, I've had similar thoughts with that too. That was kind of where I was going is it's a really nuanced question. Right. And so there's a lot of factors and then there's the factors that are like quantifiable. 714 01:03:48,680 --> 01:04:03,680 And then there are some pretty non quantifiable or hard to quantify elements like social elements. Right. We've got like, I did Lil Wayne talk about gelato in a song. Right. I mean, that just like totally skews everything. 715 01:04:03,680 --> 01:04:23,680 So there's a lot of layers to that question, but I like the avenue. I also think price, if we could get metric price data from that same timeframe, and then ratio the strains based on the average price in that timeframe, we would get a lot of great information on whether consumers were simply making an economics decision. 716 01:04:23,680 --> 01:04:30,680 And those were like easy to produce and market affordable strands. 717 01:04:30,680 --> 01:04:47,680 I love the ideas and this is what brilliant statisticians have fun at lunch talking about. And what you're basically talking about is, what do we pack into X, you know, what's our explanatory variable. 718 01:04:47,680 --> 01:04:56,680 We're trying to explain, in this case is just number of tests. And that's our proxy for popularity. 719 01:04:56,680 --> 01:05:08,680 How do we explain that right now are only explanatory variables THC doesn't seem like that's all there is to the picture. 720 01:05:08,680 --> 01:05:14,680 This is why I would love to, you know, augment the average terpene value see if there was matter. 721 01:05:14,680 --> 01:05:17,680 I love Caleb you had the idea. 722 01:05:17,680 --> 01:05:20,680 Add price. 723 01:05:20,680 --> 01:05:26,680 If I was an economist I should be. I should have thought of that one. 724 01:05:26,680 --> 01:05:31,680 Right. Everything's a function of price. 725 01:05:31,680 --> 01:05:43,680 Well, actually, price factors in, but actually, just put your that you see why I'm not an economist, but the long story short. 726 01:05:43,680 --> 01:05:58,680 Oh yeah, then the final point you had, what if a famous celebrity talked about it, and that's basically that is an explanatory variable, but it's how would you quantify that. 727 01:05:58,680 --> 01:06:02,680 So that's, that's kind of what you could. 728 01:06:02,680 --> 01:06:20,680 Maybe have fun about it right. You could actually maybe think about some way you could actually quantify that, like you could just have a counter like how many times has this strain been referenced in media. 729 01:06:20,680 --> 01:06:35,680 You could look at sales or tests over time. And if you have a suddenly a strange spikes, then probably there was some event, like a mention. 730 01:06:35,680 --> 01:06:53,680 But this is once again imperfect but like I said, it's a starting point where you could do something like, like Google Trends, you know, just try to see if you know blue dream or gelato is trending. 731 01:06:53,680 --> 01:07:06,680 The data spread is infinite you can, I love this about the data sciences, this is a great example of how it can go infinitely, we can just keep crawling for more and more information. 732 01:07:06,680 --> 01:07:23,680 And my mind is like, oh yeah we can get hashtags we could scrape social media, and network that out and have influencers have different like rating ratios for their levels influence and other things like that. 733 01:07:23,680 --> 01:07:40,680 And then we can just try to gather and manage and then mesh into this. 734 01:07:40,680 --> 01:08:03,680 So it's basically, you should have some sort of like hypothesis so maybe that's what you should all brainstorm about is, you know, what could potentially be a reason to explain the these popular strains into then Caleb you had the idea like oh, maybe it's price. 735 01:08:03,680 --> 01:08:21,680 And then you go try to get the price data. So it's, it's basically you don't just go off to get data for data sake. You first get your hypothesis, then collect the data, and then test your hypothesis. 736 01:08:21,680 --> 01:08:42,680 So, so I love it. We've got a few hypotheses to test right we can get price data, maybe some sort of popularity measure, and then I'm going to be thinking of my own hypotheses for, you know, why in the world is medical, like why are the medical popular strains 737 01:08:42,680 --> 01:08:46,680 different. 738 01:08:46,680 --> 01:09:00,680 And then, hopeful, most importantly, let me just hear. I'll just give you the rough link and you'll have to dig down. It's basically season three. 739 01:09:00,680 --> 01:09:11,680 Number 132, but the data is there on GitHub. So I encourage you all to get this data set into your hands and take a look at it yourself. 740 01:09:11,680 --> 01:09:28,680 You know this was just the first slice and dice of the data, but hopefully there's something I missed that you could find. And once again, augment this with other data sets and that could open up a lot of doors. 741 01:09:28,680 --> 01:09:45,680 On that note, thank you all for coming. Thanks for bringing your brilliant ideas, some really really really good ideas today. Thank you immensely for helping advance canvas science.