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Let's see here.

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It's good to see everyone today.

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So we can just do some introductions real quick and just see what everybody's working

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on.

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My name, of course, is Keegan, founder of CanLytics.

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And so this past week, a lot came together.

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So the software, the API is now up and running and running smooth, testing it in multiple

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programming languages at the moment, Python and Node, and working well.

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And it's funny how things work, but a piece of CanLytics code, the generation of certificates

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has now been used to generate certificates in production.

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So congratulations.

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Thanks.

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It wasn't the anticipated piece that was going to get used first.

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That's how things work.

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And so a small piece of the code has been used to generate certificates.

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And so now I'm just going to expand on that and keep improving that functionality.

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Which state?

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In Oklahoma.

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So there are some licensees out there with certificates that were partially made with

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CanLytics software.

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That's cool.

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So baby steps.

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So how about everyone else?

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Any other exciting forays into data in the past week or so?

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Not data, but quality control issues.

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What quality control has come up?

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So concentrates shouldn't taste like eggs.

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However, if sulfur is not cleaned up in the process, it can become very concentrated.

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So when it combusts, it becomes sulfur dioxide.

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It's very obvious.

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So that's a quality control error that I experienced recently.

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But the only challenge is that the people at the dispensaries have no idea what I'm

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talking about because the extraction site is many, many miles away.

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So anyway, everything was fine.

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It's just I find these quality control issues to be exciting and demand attention.

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Well, that's an interesting, relevant issue.

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So we're looking at some of the data from Washington State, looking at the lab results.

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And I thought I would just do what I preach.

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And so last week, I was saying, oh, we should look at the failure rates for the different

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solvents and microbes, microtoxins.

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So I actually went ahead and prepared those statistics today so we can at least look at

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them.

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A lot of the data looks like a lot of the potentials in cannabinoids, but I'll show

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you the failure rates as is.

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So I know Charles has done some interesting work here.

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Paul, real quick, have you done anything?

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Yeah.

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So unfortunately, most of my time has been spent writing my paper, but I should be ready

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to share some results next week.

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So I've gone through the entire process of selecting candidate dispensaries for analysis.

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And so I've gone through that process.

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I showed you a little bit of that previously.

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And I have ran through my market basket analysis for those three dispensaries, looking at the

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various combinations that emerge as far as product associations and natural product grouping.

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So I'll have something to show next week if I could do that and let you guys see what

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I've got up to.

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Definitely, definitely.

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Always interested to hear about your analysis.

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And then even if just this week anything interesting had popped up in your literature review, feel

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free to share it with us.

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And then Charles, I almost wanted to save it in case things go wrong, but I've done

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a bit of prep.

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So last week, ran out of memory as I was presenting last week some of the data and things ended

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abruptly.

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So the recording goes on.

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So if that does happen today, then just power on and maybe Charles, you can pick things

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up.

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However, I'm interested to see, hear what your analysis before I go on a spiel.

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So you wouldn't want to talk about some of this analysis that you've done recently.

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And hello, by the way, Melody.

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Hi.

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Hello.

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I've got a, I'm sorry to interrupt.

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I have an eight year old with really severe epilepsy and I'm a beginning data scientist.

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And so she's a little noisy, so I'm going to put myself on mute.

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Well, it's awesome to have you here, Melody.

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We're all learning.

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And so you're in the right place.

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So we'll just be talking about analytics coincidentally about cannabis.

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So we typically have a fun time.

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Perfect.

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Thanks.

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Thanks for having me.

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Oh, by all means.

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Welcome aboard.

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So just to introduce, Charles has been looking at some of the cannabinoid statistics from

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this data set we have in Washington state that has a lot that has essentially all of

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the lab results from the traceability system up through the end of 2020.

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We'll get the newer data set, but for the time being, we'll just look at this historic

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data.

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And so Charles, you wouldn't want to introduce what you've done real quick.

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So I've been going through the lab results data frame column by column and looking at

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the number of missing values, just sort of plotting it all out, getting trends, showing

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percentages of failures based on how they correlate to other columns and just kind of

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just basically going through it and looking at everything because trying to use a classifier

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to figure out which samples are going to fail or not.

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There's certainly some issues and it came, no matter what I tried, it always seemed to

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turn out to be just a glorified dummy classifier.

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So now I'm going through and looking and trying to figure out what data really matters and

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what data is just kind of maybe kind of, what data is too messy to use and just kind of

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going through the whole thing and just looking at it overall and so that we can look at each

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column and see what if any information is really there.

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I was doing a bit of exploratory analysis here and maybe I'll start sharing some of

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my work here.

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So essentially what I was thinking was, okay, so there's low failure rates.

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So it may be just tough to predict failures, but perhaps if a failure occurs, maybe that's

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a sign of maybe a larger problem at hand.

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So I thought maybe what would be worthwhile is, okay, let's just look at the samples that

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fail and like what's going on with them.

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So I haven't gone too much further down that repertoire, but I'll show you essentially

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how you can find those samples.

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And then perhaps Charles, you could maybe help me with this.

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If we attach all the data points, right, because we'd want to see, okay, what's the strain,

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potentially where, you know, where's, what's the geographic location.

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And it would just be interesting to see if we can't pin down some essentially other issues

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going on there.

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So, you know, do cultivators that fail, do they have, you know, lower than average cannabinoids

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like, or do they have above average moisture content, below average moisture content?

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So that was sort of my idea was, okay, let's isolate the failures and see if there's anything

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else going on with those samples.

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So do they have, you know, statistically different X, you know, moisture or what have you.

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So long story short, I'll go ahead and just start presenting some of this data.

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In doing so, we'll essentially pick back up where we got interrupted last week where we

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were replicating a cannabinoid analysis from edibles in Jamaica.

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So let's present.

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All right.

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So we're essentially working with these lab results and just to give you a look at what

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a lab result or just an observation looks like.

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We essentially have many, many data points, most of them aren't that useful.

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So the pesticides aren't reliably entered in.

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So what we, the key variables that we do have are we've got the cannabinoids principally

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THC, THCA, and CBD.

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And we're able to use some other identifiers.

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So here we can actually identify the lab.

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So this came from lab 12.

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And you can identify who, which licensee this was tested for.

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This was tested for license 413 to 87.

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And then you can even connect this to the inventory item.

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So that way you can get the strain information and perhaps more information such as harvest

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date.

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So we'll need to connect that data.

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So since we were already talking about solvents in micro, I'll go ahead and show you the analysis

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I've done there.

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And then we can move into the cannabinoid analysis where we can finish out replicating

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the analysis we were intending to replicate last week.

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So we've read in our data and then it's worthwhile to note that, okay, not all of the samples

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are the same.

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So the main distinction you have here is essentially the intermediate types.

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I did a bit more research and so essentially the flower is when it's harvested, then it's

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packaged into flower lots, which are then sold as usable marijuana.

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So what's sold at the stores is usable marijuana.

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However, they're having typically their flower lots tested.

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However, sometimes you have the flower tested immediately after the harvest, I think, before

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it's sub-lotted.

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So I think those are what those lab results are for.

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So what would be worthwhile is still, I think, to connect the lab results to what's actually

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sold at the store and maybe only look at those lab results

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because at the moment we may be including things in analysis that aren't even ever sold

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at a store.

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So right now we're just looking at the entire, essentially the entire population of cannabis

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that was tested, whether it was sold or not.

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So just to look at the concentrates real quick, essentially just grouped everything that appeared

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to be any sort of concentrated product.

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So the main ones are hydrocarbon concentrates and you also have concentrates for inhalation.

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And then I even included the non-solvent-based concentrates almost as a sanity check, as

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well as food-grade solvents, and then of course CO2 concentrates and ethanol concentrate.

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The main salt, well, not the main, all of the solvents are listed here.

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So everything from acetone down to your xylenes.

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And then I collected the limits.

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So last week I mentioned that you can find the Washington State mandated limits in their

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administrative code.

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And so essentially I got the limits that they have listed here and I put them in this spreadsheet

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using the keys provided.

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So that way we can match up, okay, acetone has a limit of 5,000 parts per million, so

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on and so forth.

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So I collected the limits and so now we've got all the data we need.

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And so for starters, I said, okay, let's just look at the average concentration of solvents

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in each concentrate.

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We'll need to define the concentrate types.

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And so we can now start to just look at essentially the baseline concentration in all the concentrates

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just to get an idea of what may or may not be present.

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And so keep in mind that certain samples may have high parts per million, whereas many

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other samples would have none.

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So you start to see that, okay, things like concentrates for inhalation, they never test

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for solvents, hydrocarbon concentrates, they're coming out across the board, low concentrations,

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but you're getting hits for the various solvents.

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What's interesting here, you are getting a few hits with the non-solvent-based concentrates.

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I included infused mix, it doesn't look like those, those may not get tested for residual

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solvents.

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And so we're just essentially just trying to just start building some conditional averages

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here just to see, okay, what solvents may or may not be present.

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So now, as I mentioned in prior meetups, that, okay, let's look at the failure rate for each

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solvent.

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Okay, so here, essentially just, essentially given a sample, so let's just, you know, given

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say a sample, so this one wouldn't have acetone, but given a sample's acetone level, we can

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compare that to the limit for acetone.

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And if it's above 5,000, it's a failure, if it's below, then it passes.

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So we'll just manually compare these to the limits and essentially try to calculate the

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failure rate.

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One second, I'm going to suppress these warnings just so it's slightly neater for us.

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So what I can show you here is there's quite, as Charles was noting in his general analysis,

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the failure rate is low.

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So your failure across the board, so your highest for hydrocarbon concentrate, your

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highest risk is butanes, and you have a less than 1% chance of failing for butane on average.

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And so here are just the conditional averages for each solvent, for each method of extraction.

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And as you'll see, I don't see any with above a 1% chance of failure, but so, so long story

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short is it may be, as Charles was noting, it may be tricky trying to predict failures,

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so it may be just more worthwhile looking at specific failures and trying to figure

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out, OK, what went wrong in this situation.

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Just to continue in this vein, we're also going to look at mycotoxins, which we also

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have limits for, and I was just going to look at some of the microbials.

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So in particular, these correspond to the enterobacteria.

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So this is what I would just generally describe as an uncleanly cultivation site.

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So I think that's what would lead to you failing for microbes.

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And so I think it's worth looking at one way or the other, so that way we at least know

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what they are.

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So if we look at mycotoxins, we'll actually see that there's really almost, I guess, almost

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no failures for mycotoxins.

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So you're seeing very low percentages.

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You're seeing a handful of flowers that are failing, as well as you've got some non-solvent

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base that may fail for okratoxin, marijuana mix.

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So essentially what I was thinking was, OK, why don't you just identify these flower lots

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and potentially see what's happening there.

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So for example, I'll write this snippet here in a bit potentially.

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We'll just do it right now.

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So essentially what we're looking for here.

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So here is all the flower data.

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And so you can essentially find all the mycotoxin failures of flower.

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You basically say, OK, let's find all the flower data where the aflatoxin was greater

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than or equal to 20.

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So you'll see, OK, so there's only 45 failures for mycotoxins.

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And so essentially what I was getting, starting to come around to is, OK, it may not be worthwhile

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trying to predict a mycotoxin failure.

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And why don't we look at these samples here and see if there's something that they have

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statistically different in some way.

236
00:25:12,760 --> 00:25:17,840
Do they have above or below average moisture?

237
00:25:17,840 --> 00:25:24,560
So here they have an average moisture of 7%.

238
00:25:24,560 --> 00:25:37,360
And so we can just say, OK, what's the average moisture content on a whole?

239
00:25:37,360 --> 00:25:47,920
So you're seeing, OK, the moisture content as a whole has an average maybe around 5%.

240
00:25:47,920 --> 00:25:56,360
So the standard deviation here is high, so it probably wouldn't be statistically significant.

241
00:25:56,360 --> 00:26:05,480
However, you could do a test and say, OK, is this mean statistically different than

242
00:26:05,480 --> 00:26:06,480
the average?

243
00:26:06,480 --> 00:26:12,160
In this case, it actually does just eyeballing it.

244
00:26:12,160 --> 00:26:16,200
Looking at the high standard deviation, it doesn't look like it's going to be statistically

245
00:26:16,200 --> 00:26:17,200
significant.

246
00:26:17,200 --> 00:26:29,040
However, if you go through the various variables and see, is there anything, is there any pattern?

247
00:26:29,040 --> 00:26:33,760
Keegan, I have a general question.

248
00:26:33,760 --> 00:26:40,720
A couple of weeks ago, you provided some analysis and some thinking behind there's a work that

249
00:26:40,720 --> 00:26:48,960
you were working with another gentleman and it was for outdoor cultivation.

250
00:26:48,960 --> 00:26:56,440
And I think there was a higher failure rates for the outdoor cultivation tests as opposed

251
00:26:56,440 --> 00:27:00,640
to what we're looking at for Washington State, which I believe we're looking at like the

252
00:27:00,640 --> 00:27:04,440
indoor grows here.

253
00:27:04,440 --> 00:27:09,800
Have you noticed between, and there's two different ways of growing, but have you noticed

254
00:27:09,800 --> 00:27:16,080
between different states just kind of more systemic failure rates, you know, like looks

255
00:27:16,080 --> 00:27:18,680
like here in Washington State is pretty low.

256
00:27:18,680 --> 00:27:25,360
Do they all tend to be pretty low for the various states you've seen or?

257
00:27:25,360 --> 00:27:29,240
Anecdotally, I've heard various things.

258
00:27:29,240 --> 00:27:36,660
So I think it varies state by state.

259
00:27:36,660 --> 00:27:40,440
So it matters and the regulations matter.

260
00:27:40,440 --> 00:27:49,080
So it's not a state, but for example, I think I've heard that failure rates may be high

261
00:27:49,080 --> 00:27:50,080
in Canada.

262
00:27:50,080 --> 00:27:57,000
And so a lot of it depends on, okay, what are you testing or screening for?

263
00:27:57,000 --> 00:28:01,640
And what is the threshold, the limit for failure?

264
00:28:01,640 --> 00:28:12,760
So the lower your limit and the more, you know, pathogens or analytes that you're screening

265
00:28:12,760 --> 00:28:17,200
for, the higher your failure rates are going to be.

266
00:28:17,200 --> 00:28:23,040
Yeah, it seems like there's a lot of, from a public health perspective, it seems like

267
00:28:23,040 --> 00:28:29,000
there's a lot of wiggle room for variation across all the different states, right?

268
00:28:29,000 --> 00:28:36,760
It seems like that there's, I don't know, not a single standard, obviously, the states

269
00:28:36,760 --> 00:28:42,160
run it their own way, but I don't know, it seems like there's a lot of areas where things

270
00:28:42,160 --> 00:28:48,720
it's going to be hard to really know from a consumer perspective, just, you know, how

271
00:28:48,720 --> 00:28:53,640
safe is my product going from state to state?

272
00:28:53,640 --> 00:28:59,400
Well, I was going to give them a shout out real quick, but we'll have to revisit that.

273
00:28:59,400 --> 00:29:07,960
So I think that's fruitful analysis.

274
00:29:07,960 --> 00:29:20,360
I'm not 100% certain how you would frame your research question, but essentially, what I

275
00:29:20,360 --> 00:29:34,800
think you'd be trying to uncover is, okay, are like the regulations, I guess, guiding,

276
00:29:34,800 --> 00:29:41,400
I guess, I guess like the argument put forth has been, okay, so say in California, they

277
00:29:41,400 --> 00:29:49,880
have strict regulations on microbe levels and pesticide levels.

278
00:29:49,880 --> 00:30:00,200
The idea is that cultivators in those states get skilled at keeping their microbes low

279
00:30:00,200 --> 00:30:07,880
in their pesticides low.

280
00:30:07,880 --> 00:30:15,640
So I know at the time, you know, the recreational marijuana was legalized in Oregon, Oregon

281
00:30:15,640 --> 00:30:20,560
was supposed to have the strictest standards.

282
00:30:20,560 --> 00:30:26,080
And certainly, you know, there were much higher standard than what was in Washington and Colorado

283
00:30:26,080 --> 00:30:27,940
at the time.

284
00:30:27,940 --> 00:30:31,360
So I don't know, I mean, a lot of other states have legalized it since then.

285
00:30:31,360 --> 00:30:37,680
So I don't know if those states have enacted stricter standards or if they're more lax

286
00:30:37,680 --> 00:30:40,720
or if they're the same.

287
00:30:40,720 --> 00:30:49,360
But I guess I would actually, that's probably easily obtainable information.

288
00:30:49,360 --> 00:30:51,360
And that might be a good comparison.

289
00:30:51,360 --> 00:30:56,920
Yeah, it's just was, I don't want to derail the conversation too much, but it just the

290
00:30:56,920 --> 00:31:01,720
analysis that you're showing, Keegan, just got me thinking and just the standardization,

291
00:31:01,720 --> 00:31:05,240
I guess, is the word I'm thinking of here.

292
00:31:05,240 --> 00:31:12,160
Well, you brought it up in the prime time, because here we are essentially talking about

293
00:31:12,160 --> 00:31:13,160
microbes.

294
00:31:13,160 --> 00:31:17,600
And this is what the microbiologists are stressing.

295
00:31:17,600 --> 00:31:23,640
So you've got people in a lot of different camps here.

296
00:31:23,640 --> 00:31:31,280
And so there are people in the camp that think that we're not really screening cannabis enough

297
00:31:31,280 --> 00:31:36,520
or for the right pathogens.

298
00:31:36,520 --> 00:31:46,640
And so there are people in the camp that think, okay, we need to be screening more essentially.

299
00:31:46,640 --> 00:31:53,160
And then there are essentially some people in the camp, I met them, so they're basically

300
00:31:53,160 --> 00:32:07,360
saying like, okay, it doesn't necessarily make sense to be screening everything for,

301
00:32:07,360 --> 00:32:11,680
I don't want to put words in people's mouths, but for all of these pathogens.

302
00:32:11,680 --> 00:32:13,800
And so I'm not certain which ones per se.

303
00:32:13,800 --> 00:32:19,040
So like I said, you do see failures for certain ones of them.

304
00:32:19,040 --> 00:32:24,760
But it seems that, okay, maybe we don't necessarily need to be testing for microtoxins.

305
00:32:24,760 --> 00:32:30,880
And like I said, I don't want to put words in people's mouths, but I'm not certain which

306
00:32:30,880 --> 00:32:37,200
ones, but the microtoxins are potentially some of the microbes like salmonella.

307
00:32:37,200 --> 00:32:43,400
I don't think you really see samples failing for those.

308
00:32:43,400 --> 00:32:53,440
Whereas perhaps there are other contaminants that may be worthwhile screening for like

309
00:32:53,440 --> 00:33:02,440
perhaps, so in Washington state, they don't really have the yeast and molds to my understanding.

310
00:33:02,440 --> 00:33:09,600
So I think just from what I've picked up, it seems that, okay, maybe we're screening

311
00:33:09,600 --> 00:33:20,760
for the wrong contaminants and not across the board, but some of them may be excessively,

312
00:33:20,760 --> 00:33:26,040
we may be doing excessive tests for microbes that aren't really expected to be there.

313
00:33:26,040 --> 00:33:33,520
And we may not be testing for microbes that are worthwhile to be screening for.

314
00:33:33,520 --> 00:33:38,240
Yeah, it seems like time will tell what the consensus is on a lot of these things and

315
00:33:38,240 --> 00:33:45,360
whether the federal government gets involved on overall standards and that sort of thing.

316
00:33:45,360 --> 00:33:46,360
Exactly.

317
00:33:46,360 --> 00:33:52,880
And so perhaps even for next week, since I always try to pick up sort of an interesting

318
00:33:52,880 --> 00:33:58,600
topic in the week that I don't actually know enough about and try to do to research it

319
00:33:58,600 --> 00:34:00,120
for the next week.

320
00:34:00,120 --> 00:34:09,080
So I could start looking into some of people's perspectives on, especially in the lab space

321
00:34:09,080 --> 00:34:11,240
on microbes.

322
00:34:11,240 --> 00:34:16,720
So what should maybe be tested more, what should maybe be tested less, what do we have

323
00:34:16,720 --> 00:34:17,920
right?

324
00:34:17,920 --> 00:34:19,480
What states have it right?

325
00:34:19,480 --> 00:34:21,800
What states have it wrong?

326
00:34:21,800 --> 00:34:27,320
And so then we can almost do a state by state comparison of the various regulations and

327
00:34:27,320 --> 00:34:34,640
see and even how those regulations have made may have changed over time, because in Washington

328
00:34:34,640 --> 00:34:42,360
State, they changed which microbes were being screened for.

329
00:34:42,360 --> 00:34:46,120
And this may have happened in other states.

330
00:34:46,120 --> 00:34:47,120
So I think.

331
00:34:47,120 --> 00:34:51,040
Yeah, I could see if you went down that path.

332
00:34:51,040 --> 00:34:58,400
And I don't know if other analytical outfits are doing this type of work.

333
00:34:58,400 --> 00:35:05,160
But if you were able to pull that information together to a certain level, it almost be

334
00:35:05,160 --> 00:35:12,920
like, can it could be in an advisory position based on that knowledge that you've compiled,

335
00:35:12,920 --> 00:35:13,920
right?

336
00:35:13,920 --> 00:35:19,680
Because I just see, you know, not that I'm not very knowledgeable in this area, but I

337
00:35:19,680 --> 00:35:22,400
could see that the force is pushing in that direction.

338
00:35:22,400 --> 00:35:27,520
And if you if you position yourself with that information, it might be a good resource for

339
00:35:27,520 --> 00:35:28,520
others.

340
00:35:28,520 --> 00:35:33,800
That's definitely something that we want to incorporate in the analytics platform.

341
00:35:33,800 --> 00:35:43,520
So essentially, we see the lab testing community as really just one big community of labs,

342
00:35:43,520 --> 00:35:44,520
right?

343
00:35:44,520 --> 00:35:51,560
We've got labs all across the country, and a lot of them are doing the very similar things,

344
00:35:51,560 --> 00:35:52,560
right?

345
00:35:52,560 --> 00:35:59,880
So here you've got labs in Oklahoma, and they're testing cannabinoids, they're testing pesticides,

346
00:35:59,880 --> 00:36:05,040
just like people in Washington State, or just like people in Colorado, just like people

347
00:36:05,040 --> 00:36:06,040
in Michigan.

348
00:36:06,040 --> 00:36:08,040
And exactly.

349
00:36:08,040 --> 00:36:15,280
And so we essentially have a platform here that can handle, okay, yes, the limits may

350
00:36:15,280 --> 00:36:20,960
vary state by state, but you're still doing the same process.

351
00:36:20,960 --> 00:36:33,240
So yeah, so in general terms, in any kind of market, the folks that develop a platform,

352
00:36:33,240 --> 00:36:37,440
you can think of, you know, Uber and search engines and anything else you can come up

353
00:36:37,440 --> 00:36:38,440
with.

354
00:36:38,440 --> 00:36:43,880
But those who have the platform that connects all the different groups together based on

355
00:36:43,880 --> 00:36:48,080
what it is they're trying to accomplish usually ends up being in a pretty good position.

356
00:36:48,080 --> 00:36:56,640
And just to to toot our horn real quick, since it is up and running, can essentially show

357
00:36:56,640 --> 00:37:01,920
you essentially what that would look like, just its simplest version.

358
00:37:01,920 --> 00:37:09,120
So essentially, you know, you'd have your set of analytes.

359
00:37:09,120 --> 00:37:26,300
And so, you know, for example, let's see if we can't find a nice microbe.

360
00:37:26,300 --> 00:37:36,400
So aspergillus, so, you know, for example, you know, this limit, you know, I've got to

361
00:37:36,400 --> 00:37:38,160
set it one part per million.

362
00:37:38,160 --> 00:37:42,320
So I think so.

363
00:37:42,320 --> 00:37:49,320
So I think they've got, you know, one part per million restriction on aspergillus in

364
00:37:49,320 --> 00:37:50,320
Oklahoma.

365
00:37:50,320 --> 00:37:57,000
I just use that as a model for these limits.

366
00:37:57,000 --> 00:38:03,960
Let's see if there's any other.

367
00:38:03,960 --> 00:38:07,160
So you would be able to look across all the various different companies that are using

368
00:38:07,160 --> 00:38:13,880
your platform and and then across one particular analyte here, you would be able to see all

369
00:38:13,880 --> 00:38:16,920
the different limits that they're using.

370
00:38:16,920 --> 00:38:18,760
Exactly.

371
00:38:18,760 --> 00:38:22,480
And so we would even want to try to help out a little.

372
00:38:22,480 --> 00:38:28,320
But essentially, you know, the limits are going to vary state by state.

373
00:38:28,320 --> 00:38:30,600
Everybody's essentially doing the same thing.

374
00:38:30,600 --> 00:38:36,840
And yes, it would be then interesting to then measure like, OK, do those limits then have

375
00:38:36,840 --> 00:38:37,920
an effect?

376
00:38:37,920 --> 00:38:45,480
So are people failing samples more or less in some states or others, depending on the

377
00:38:45,480 --> 00:38:46,480
limit?

378
00:38:46,480 --> 00:38:59,840
And I think the reason that would be significant is there's a truncation effect that they talk

379
00:38:59,840 --> 00:39:02,040
about in economics.

380
00:39:02,040 --> 00:39:19,760
So whenever you whenever you're running right up to the threshold, that can affect your

381
00:39:19,760 --> 00:39:22,120
analysis for starters.

382
00:39:22,120 --> 00:39:29,800
And so the reason I feel like that's relevant here is OK.

383
00:39:29,800 --> 00:39:35,200
So you're actually let's pull up this.

384
00:39:35,200 --> 00:39:36,200
The Midwestern.

385
00:39:36,200 --> 00:39:45,640
And database here real quick, because I think this is a good example of exactly what I'm

386
00:39:45,640 --> 00:39:47,920
talking about here.

387
00:39:47,920 --> 00:39:48,920
So.

388
00:39:48,920 --> 00:40:01,200
So here, essentially, this red the red line is you can think about that as your failure

389
00:40:01,200 --> 00:40:07,320
rate for for cannabinoids and hemp.

390
00:40:07,320 --> 00:40:12,680
And so you would expect that this would be similar for.

391
00:40:12,680 --> 00:40:17,480
So let's just pretend that this is for for various other compounds.

392
00:40:17,480 --> 00:40:22,560
So to say this is for salmonella or something.

393
00:40:22,560 --> 00:40:29,760
So in this case, and I think at the federal level, they are looking at adjusting the threshold

394
00:40:29,760 --> 00:40:31,000
to THC.

395
00:40:31,000 --> 00:40:33,320
So in hemp.

396
00:40:33,320 --> 00:40:40,840
So this would be a limit where I think it would lead to misleading outcomes, because

397
00:40:40,840 --> 00:40:50,720
you're basically saying that, OK, something on this side of the red line is passing hemp.

398
00:40:50,720 --> 00:40:57,360
And then something on the exact other side of the line is failing hemp.

399
00:40:57,360 --> 00:41:00,260
And so.

400
00:41:00,260 --> 00:41:05,640
You can you can get misleading results, essentially.

401
00:41:05,640 --> 00:41:12,000
And my concern is, you know, what if this was happening with like you wouldn't want

402
00:41:12,000 --> 00:41:15,280
to see this happening with a microbe?

403
00:41:15,280 --> 00:41:21,920
So if you had something like this with your microbe where there's some.

404
00:41:21,920 --> 00:41:25,080
You know, a lot on one side and not many on the other.

405
00:41:25,080 --> 00:41:33,000
So your point is you're basically going to have things that they look like they passed,

406
00:41:33,000 --> 00:41:36,600
but they're really just shy of failing.

407
00:41:36,600 --> 00:41:37,600
Right.

408
00:41:37,600 --> 00:41:39,280
Yeah, there you go.

409
00:41:39,280 --> 00:41:44,120
So hard, hard concept I'm trying to convey.

410
00:41:44,120 --> 00:41:49,440
But basically.

411
00:41:49,440 --> 00:41:50,440
How is that?

412
00:41:50,440 --> 00:41:53,160
You know, how do states want to set their thresholds?

413
00:41:53,160 --> 00:42:00,200
And so, like, obviously, in Washington state, it looks like the bar for microbes is really

414
00:42:00,200 --> 00:42:01,200
high.

415
00:42:01,200 --> 00:42:04,840
And so let's just kind of throw these axes out the window.

416
00:42:04,840 --> 00:42:10,520
But basically, you know, the red bar is not capturing any of the.

417
00:42:10,520 --> 00:42:16,280
Of anything, even if it may have high microbes, whereas in other states, they may lower the

418
00:42:16,280 --> 00:42:18,400
threshold.

419
00:42:18,400 --> 00:42:26,720
But then you may wind up in this scenario where you're essentially failing samples that

420
00:42:26,720 --> 00:42:31,400
look quite similar to samples that pass.

421
00:42:31,400 --> 00:42:32,400
Yeah.

422
00:42:32,400 --> 00:42:40,200
Yeah, there's it's not not subjectivity, but there's.

423
00:42:40,200 --> 00:42:54,520
Certain level of reasonableness that you have to apply to these thresholds.

424
00:42:54,520 --> 00:42:55,520
Exactly.

425
00:42:55,520 --> 00:43:01,360
I mean, I just I guess maybe this is because I coming from the economics point of view,

426
00:43:01,360 --> 00:43:06,240
I think, OK, well, it just comes down to a cost benefit analysis.

427
00:43:06,240 --> 00:43:13,920
You know, what's the you know, obviously, there is a cost of increased testing and,

428
00:43:13,920 --> 00:43:15,920
you know, there's a benefit as well.

429
00:43:15,920 --> 00:43:21,880
So, you know, they said essentially the, you know, the public safety.

430
00:43:21,880 --> 00:43:27,160
And so from the economics point of view, I think it would just come down to a cost benefit

431
00:43:27,160 --> 00:43:33,720
analysis where you just try to measure the benefit to the public and try to balance that

432
00:43:33,720 --> 00:43:39,760
with the cost of.

433
00:43:39,760 --> 00:43:48,240
Essentially more testing and then the cost to cultivators for having to jump through all

434
00:43:48,240 --> 00:43:52,040
the hoops necessary to keep.

435
00:43:52,040 --> 00:43:53,440
Right.

436
00:43:53,440 --> 00:43:56,040
To keep your failures low.

437
00:43:56,040 --> 00:43:57,040
So right.

438
00:43:57,040 --> 00:44:02,080
But that's that's where I'm coming from.

439
00:44:02,080 --> 00:44:04,000
Different people come from this from different places.

440
00:44:04,000 --> 00:44:05,000
Right.

441
00:44:05,000 --> 00:44:10,280
So you've got the regulators looking at this situation and they weigh the public safety

442
00:44:10,280 --> 00:44:13,280
highly.

443
00:44:13,280 --> 00:44:18,440
And then, you know, the businesses are coming from it from their point of view and they're

444
00:44:18,440 --> 00:44:21,960
they're going to be focused about the costs.

445
00:44:21,960 --> 00:44:26,880
So good stuff.

446
00:44:26,880 --> 00:44:30,000
So anyways, we went down that rabbit hole.

447
00:44:30,000 --> 00:44:32,840
So just to get back to the data real quick.

448
00:44:32,840 --> 00:44:38,560
And then in the last bit of time here, I'll just kind of wrap up that cannabinoid analysis

449
00:44:38,560 --> 00:44:41,520
because I thought that was kind of interesting.

450
00:44:41,520 --> 00:44:51,720
But here's just a look at the microbes real quick by by sample type.

451
00:44:51,720 --> 00:44:54,960
What I noticed was interesting here.

452
00:44:54,960 --> 00:44:56,760
So we'll come back to this in a second.

453
00:44:56,760 --> 00:45:06,440
So if you look across the board, you'll see, OK, you know, you're having some some flower

454
00:45:06,440 --> 00:45:12,080
lots fail for microbes.

455
00:45:12,080 --> 00:45:17,760
And then you're actually having, you know, above, you know, slightly higher marijuana

456
00:45:17,760 --> 00:45:21,200
mix failing for microbes.

457
00:45:21,200 --> 00:45:26,240
And from my experience, this is essentially what you'd expect.

458
00:45:26,240 --> 00:45:33,960
So a lot of the times the marijuana mix just ends up being some of the anecdotally I've

459
00:45:33,960 --> 00:45:41,360
heard ends up just being some of the like the the loose bits that may be kind of left

460
00:45:41,360 --> 00:45:47,560
over after after trimming or after packaging.

461
00:45:47,560 --> 00:45:53,720
So they may get everything packaged and they just may package up all the loose bits as

462
00:45:53,720 --> 00:45:55,600
marijuana mix.

463
00:45:55,600 --> 00:46:00,640
And so when you would expect when you do that, things are going to there's just going to

464
00:46:00,640 --> 00:46:05,520
be more microbes in in that mix.

465
00:46:05,520 --> 00:46:11,960
So they tend to fail slightly higher than average for microbes.

466
00:46:11,960 --> 00:46:16,560
And then the other thing I was going to point out that I thought was interesting in this,

467
00:46:16,560 --> 00:46:22,560
you may need to drill down a bit because summary statistics can be misleading.

468
00:46:22,560 --> 00:46:30,640
But I thought, oh, like there's a you know, there's, you know, a non negligible portion of non mandatory

469
00:46:30,640 --> 00:46:34,720
samples failing for for micro.

470
00:46:34,720 --> 00:46:41,800
And so what that looks like me is like maybe maybe people may suspect that they may have

471
00:46:41,800 --> 00:46:44,560
a dirty environment.

472
00:46:44,560 --> 00:46:50,640
And they they're trying to do just some some almost like research and development to try

473
00:46:50,640 --> 00:46:53,360
to get things cleaned up proactively.

474
00:46:53,360 --> 00:47:01,760
But but don't want to attach too much of a narrative to that because I think further

475
00:47:01,760 --> 00:47:05,080
drilling is needed.

476
00:47:05,080 --> 00:47:22,800
So the non mandatory plant sample is makes up zero point zero zero point zero one two percent of the samples.

477
00:47:22,800 --> 00:47:29,440
Exactly. So it may even it's not it may almost be an insignificant amount of the sample.

478
00:47:29,440 --> 00:47:31,200
So so yeah.

479
00:47:31,200 --> 00:47:38,920
So I mean, one failure could, you know, you could make it look really statistically significant

480
00:47:38,920 --> 00:47:45,480
when there might only be one or two or maybe like 10 samples or something.

481
00:47:45,480 --> 00:47:47,440
It's not a whole lot.

482
00:47:47,440 --> 00:47:50,760
Exactly. And so it's an outlier.

483
00:47:50,760 --> 00:48:00,280
And so I think, yeah, instead of chasing chasing that, you I think what's worthwhile here is,

484
00:48:00,280 --> 00:48:05,840
OK, maybe you may want to look at the flowers and the marijuana mixes and see if there's

485
00:48:05,840 --> 00:48:12,200
any pattern that you could uncover, because basically what are you trying to do here?

486
00:48:12,200 --> 00:48:19,800
You're trying to find some factor that may help the cultivators have a cleaner environment.

487
00:48:19,800 --> 00:48:25,160
So if you just if you just look at all the failures and you notice they're all in this,

488
00:48:25,160 --> 00:48:35,080
like this is like the classic example of statistics. And I unfortunately don't remember.

489
00:48:35,080 --> 00:48:43,960
The. The name of the statistician, but.

490
00:48:43,960 --> 00:48:50,240
But basically, they were looking at, I think, cholera outbreaks and they were looking at wells

491
00:48:50,240 --> 00:48:55,320
and they said, oh, they realized, oh, many people that frequent this well, you know,

492
00:48:55,320 --> 00:48:59,640
they're they're suffering. And so you could essentially try to do that with cannabis.

493
00:48:59,640 --> 00:49:06,880
So you could say, OK, let's look at all the failures for microbes and try to uncover a pattern.

494
00:49:06,880 --> 00:49:15,560
So are all the microbial failures, are they right beside cow pastures or.

495
00:49:15,560 --> 00:49:22,440
What? So there may be there may be just being nothing going on there,

496
00:49:22,440 --> 00:49:28,640
but I think it's worthwhile trying to find out because.

497
00:49:28,640 --> 00:49:34,080
Let's say you discover something and you're like, OK, people beside cow pastures

498
00:49:34,080 --> 00:49:38,120
have a 15 percent greater chance of failing for microbes.

499
00:49:38,120 --> 00:49:45,200
Well, if cultivators knew that, then they may not want to set up shop right beside a cow pasture.

500
00:49:45,200 --> 00:49:52,840
And so it's just, you know, the more you know, the better operation you can run.

501
00:49:52,840 --> 00:50:01,680
That's my spiel there. And so now let's go ahead and finish out the cannabinoids from from the priority.

502
00:50:01,680 --> 00:50:10,120
So we're Heather and Paul, just to show you what we're doing here.

503
00:50:10,120 --> 00:50:18,520
Basically, just look in the literature, trying to find an analysis that people were doing.

504
00:50:18,520 --> 00:50:23,400
Saw that there was a recent paper here, July 10th.

505
00:50:23,400 --> 00:50:29,200
About people looking at edibles in Jamaica.

506
00:50:29,200 --> 00:50:36,880
And what jumped out to me was, OK, they actually only have, you know, 45, a sample of 45,

507
00:50:36,880 --> 00:50:40,640
which is better than nothing.

508
00:50:40,640 --> 00:50:47,840
And so, you know, they were just looking at the THC CBD concentrations.

509
00:50:47,840 --> 00:50:51,760
And just to show you a chart.

510
00:50:51,760 --> 00:50:56,640
They're just measuring these in the lab.

511
00:50:56,640 --> 00:51:02,000
And so then they get their THC CBD ratio in the edibles.

512
00:51:02,000 --> 00:51:16,680
And so essentially thought, OK, we can replicate their study in Washington state and just look at the THC CBD ratio in edibles here in Washington.

513
00:51:16,680 --> 00:51:25,320
Just to, you know, just a demonstration of what we can do and how powerful our analysis is.

514
00:51:25,320 --> 00:51:31,880
So we can go ahead and isolate the solid edibles.

515
00:51:31,880 --> 00:51:36,560
And, you know, just to toot our horns a little bit.

516
00:51:36,560 --> 00:51:45,720
So this is what's cool about Washington state's public data is over the prior years.

517
00:51:45,720 --> 00:51:58,680
And this is where we crashed last week was we were basically saw, OK, you know, from 2018 to 2021, we have almost 9000.

518
00:51:58,680 --> 00:52:07,360
Solid edible samples. And so, you know, we can do some some some good analysis here.

519
00:52:07,360 --> 00:52:15,600
Quick note.

520
00:52:15,600 --> 00:52:36,680
Inevitable are coded as either percent or milligrams per gram. It looks like a handful, more than a handful, 460 samples are coded with THC percent.

521
00:52:36,680 --> 00:52:43,960
And the rest were coded with milligrams per gram.

522
00:52:43,960 --> 00:52:49,160
And so you can actually convert percent to milligrams per gram.

523
00:52:49,160 --> 00:52:54,280
So when you think about it, there's a thousand.

524
00:52:54,280 --> 00:53:03,920
Milligrams in a gram. So, you know, if something is 10 percent.

525
00:53:03,920 --> 00:53:09,920
THC, well, then that's going to be a hundred milligrams.

526
00:53:09,920 --> 00:53:14,560
Per a thousand milligrams or per gram.

527
00:53:14,560 --> 00:53:17,520
So that would be a hundred milligrams per gram.

528
00:53:17,520 --> 00:53:24,800
Ten percent. So you can convert.

529
00:53:24,800 --> 00:53:34,400
Percentages. So say you've got 10 percent here.

530
00:53:34,400 --> 00:53:44,360
You've got 10 percent there. You can convert 10 percent to milligrams per gram by just multiplying by 10.

531
00:53:44,360 --> 00:53:49,960
So now you just take your 10 percent, you multiply it by 10.

532
00:53:49,960 --> 00:53:54,960
You now have a hundred milligrams per gram.

533
00:53:54,960 --> 00:54:07,040
So. The only thing that hung me up was just coding it up real quick, but we can basically convert everything that's missing.

534
00:54:07,040 --> 00:54:15,960
Milligrams per gram, but does have the percentage and we can just put everything in milligrams per gram.

535
00:54:15,960 --> 00:54:26,680
Just for the sake of time, I have not actually solved this yet, so if anyone wants to try to solve this line of code, then by all means.

536
00:54:26,680 --> 00:54:32,080
Otherwise, I'll look at it for next week. So long story short.

537
00:54:32,080 --> 00:54:39,560
We can look at the THC and CBD in edibles here.

538
00:54:39,560 --> 00:54:53,480
And so. Only. Probably going to need this chart up here.

539
00:54:53,480 --> 00:54:59,840
And then hopefully this just works.

540
00:54:59,840 --> 00:55:13,400
So. Just we're quick. Essentially, we were just saying, OK, can we essentially replicate their study, but do it in Washington?

541
00:55:13,400 --> 00:55:19,240
And essentially we have so.

542
00:55:19,240 --> 00:55:30,120
Keep in mind here, I'm dropping outliers, so I'm dropping the top 5 percent because you see things that.

543
00:55:30,120 --> 00:55:41,280
Have. Potentially unrealistic levels of THC and CBD, so I was just dropping the outliers.

544
00:55:41,280 --> 00:55:47,480
So excluding the outliers so we can actually get a decent looking chart.

545
00:55:47,480 --> 00:55:52,600
Let's compare this to.

546
00:55:52,600 --> 00:56:06,000
Oh, actually. I may be able to do is I may have printed this off.

547
00:56:06,000 --> 00:56:15,360
OK, so it doesn't look like it printed correctly, so.

548
00:56:15,360 --> 00:56:20,080
Just trying to get these charts side by side to a decent.

549
00:56:20,080 --> 00:56:30,120
So bear with me here.

550
00:56:30,120 --> 00:56:45,320
OK. So. We've got we have similar charts, so we're both seeing that, OK, the THC to CBD ratio, it's typically less than 50.

551
00:56:45,320 --> 00:56:53,400
And so their mean was 29 was around 30.

552
00:56:53,400 --> 00:57:02,560
Milligrams of THC to one milligram of CBD, we're seeing that it's around 20 to one.

553
00:57:02,560 --> 00:57:08,320
There's standard deviation 54 hours is 34 or.

554
00:57:08,320 --> 00:57:11,000
Around 33.

555
00:57:11,000 --> 00:57:19,400
So sorry, Keegan, I'm sorry to interrupt you. I do have to drop for some work related activities, but I'll give you a call later today if you're available.

556
00:57:19,400 --> 00:57:23,640
I wanted to talk to you about a couple of things about the graduate project.

557
00:57:23,640 --> 00:57:28,160
Definitely, definitely. And we'll be wrapping up here in a second, but thank you for attending, Paul.

558
00:57:28,160 --> 00:57:37,480
And definitely, and if anyone else has questions about some of the analytics we're doing here, just always, always happy to talk.

559
00:57:37,480 --> 00:57:42,160
Hot cannabis data. All right. Thanks guys. We'll talk to you soon. Bye bye.

560
00:57:42,160 --> 00:57:45,840
Thank you. Bye.

561
00:57:45,840 --> 00:57:50,080
Just for everyone else to go ahead and bring this home here.

562
00:57:50,080 --> 00:58:00,080
So essentially. Essentially, what we're just doing is we're just saying, OK, yes, you know, not only.

563
00:58:00,080 --> 00:58:12,240
Can we look at so basically we can start looking at analyses that other people have done and we can potentially replicate them in Washington state with.

564
00:58:12,240 --> 00:58:22,160
A greater amount of data. Of course, it's different. You know, they're looking at Jamaica and we're looking at Washington state.

565
00:58:22,160 --> 00:58:28,160
However, we're both looking at cannabis edibles, so there could be things to uncover.

566
00:58:28,160 --> 00:58:38,360
So this was just a simple analysis here where we're just, you know, we're just looking at the THC to CBD ratio.

567
00:58:38,360 --> 00:58:44,000
But now we know. And so so how is this valuable?

568
00:58:44,000 --> 00:58:49,080
Well, so now if you're a.

569
00:58:49,080 --> 00:59:03,960
A processor, an edible manufacturer, well, now you can know that, OK, most people are producing edibles that are less than, you know, 50 to one THC to CBD.

570
00:59:03,960 --> 00:59:15,800
And, you know, dialing in your ideal ratio is is often part of the challenge for the edible manufacturers.

571
00:59:15,800 --> 00:59:25,720
So it's a tricky thing to do. And so.

572
00:59:25,720 --> 00:59:32,000
So, so I think this could could be worthwhile, worthwhile analysis.

573
00:59:32,000 --> 00:59:41,280
And like I said, this is just a start. So, you know, now you have a whole nother variable here, you know, your THC to CBD ratio.

574
00:59:41,280 --> 00:59:52,280
So now you can once again, you can do analysis and OK, do any of these other factors we're looking at, you know, do those influence.

575
00:59:52,280 --> 00:59:59,080
Influence ratio. No, so.

576
00:59:59,080 --> 01:00:05,280
There is a lot more to be done here with.

577
01:00:05,280 --> 01:00:13,720
Analytics of cannabinoids, so I think I'll go ahead and conclude there because.

578
01:00:13,720 --> 01:00:21,080
There's so much to do that. You know, you may have to start looking through some of this on your own.

579
01:00:21,080 --> 01:00:26,520
So.

580
01:00:26,520 --> 01:00:35,240
But. Real quick in the last few minutes, any questions, comments, ideas?

581
01:00:35,240 --> 01:00:45,800
I just I guess I don't mean to be dramatic, I just feel a little moved by the last graph that you showed with the red line and the.

582
01:00:45,800 --> 01:00:50,560
The lack of what I think is a suitable distribution around the failure point.

583
01:00:50,560 --> 01:00:54,120
You know, all your points being clustered, I mean, that's extremely unsettling.

584
01:00:54,120 --> 01:01:00,520
I'm going to say that I've been privy to that in the lab directly, but not when it really matters like this.

585
01:01:00,520 --> 01:01:06,560
Like, well, I don't know. Keep in mind that was hemp we were looking at.

586
01:01:06,560 --> 01:01:17,360
So that was hemp. I understand. But it is it is something that I'm sure the hemp producers would appreciate you being up in arms about that because.

587
01:01:17,360 --> 01:01:23,480
It's just tough, right? Because like, say you're a hemp producer and you tested it point three one percent.

588
01:01:23,480 --> 01:01:29,840
And then you're the guy down the road or the girl down the road, tested at point two nine percent.

589
01:01:29,840 --> 01:01:38,640
And you've got to destroy your harvest and then they get to go on and make a small fortune.

590
01:01:38,640 --> 01:01:45,640
Yeah, that can that can hurt. So now you need to start setting up cameras like if I'm going to be growing my own stuff,

591
01:01:45,640 --> 01:01:51,680
test it just before you chop or as you're chopping better be on camera.

592
01:01:51,680 --> 01:01:55,920
Like, when are you testing? You know, I just there's just so many things to consider.

593
01:01:55,920 --> 01:02:00,600
Maryland state, for example, flower test super high in Maryland. And I don't know why.

594
01:02:00,600 --> 01:02:06,600
It's also very, very dry. So when the numbers that you're referring to at times, they seem low.

595
01:02:06,600 --> 01:02:11,280
But that's because of the numbers that we're looking at in Maryland that are very standard.

596
01:02:11,280 --> 01:02:15,320
So it's it's not to say good or bad. It's just what gives rise to that.

597
01:02:15,320 --> 01:02:18,520
The method of testing. There's so many things that go into the testing.

598
01:02:18,520 --> 01:02:22,840
You're referring to testing edibles, testing the center of it. Oh, no.

599
01:02:22,840 --> 01:02:30,800
Like, it's so complicated. I'm not saying it's overwhelming, but I'm I'm intrigued more and I just want to keep on listening.

600
01:02:30,800 --> 01:02:34,840
Oh, you bring up interesting points in the mid to edibles. That's what people are always asking.

601
01:02:34,840 --> 01:02:44,440
So typically what they have to do is send in the entire edible and then they'll they'll they'll they'll they'll dilute or they'll dissolve it.

602
01:02:44,440 --> 01:02:49,480
Create a dilution, then test that. And so, you know, people are always what it's always a question.

603
01:02:49,480 --> 01:02:55,920
Do I test the inputs? And then you raise interesting point with hemp.

604
01:02:55,920 --> 01:03:06,920
When do you send in the sample? So maybe some people have techniques that they know when to sample.

605
01:03:06,920 --> 01:03:13,600
To to make sure that they're they're staying below the permitted THC levels.

606
01:03:13,600 --> 01:03:21,520
And maybe some people are naive and they may not know. And so it's a tricky dance to dance.

607
01:03:21,520 --> 01:03:32,640
And my only consolidation to your consolation to to help producers is I as far as like I thought they had raised it for a second,

608
01:03:32,640 --> 01:03:42,840
but it looks like they may not have. But there's definitely noise about them raising the the hemp requirement to I think point six or point seven five.

609
01:03:42,840 --> 01:03:49,280
And so I mean, you saw the chart. And so if they did raise the hemp.

610
01:03:49,280 --> 01:03:58,000
Permitted THC to point six or point seven five, then that would allow a lot more hemp into the market.

611
01:03:58,000 --> 01:04:05,480
They know the conversion rate is a like point oh three percent that you're talking about with THC for certain states.

612
01:04:05,480 --> 01:04:09,480
Is that based on the conversion rate that they know?

613
01:04:09,480 --> 01:04:18,480
So that's after essentially you apply conversion rate to THC, a because THC decarboxylates into THC.

614
01:04:18,480 --> 01:04:33,920
So technically your total THC, which is Delta nine THC plus zero point eight seven seven times your THC.

615
01:04:33,920 --> 01:04:45,320
The combination of those is your total THC. So your total THC for hemp has to be less than zero point three percent.

616
01:04:45,320 --> 01:04:54,000
Meaning to say the their concern is that if there's beyond a certain level, that it will the THC level will go up beyond a certain point.

617
01:04:54,000 --> 01:04:57,880
Do they know that potential?

618
01:04:57,880 --> 01:05:03,200
I think. Yeah, sorry.

619
01:05:03,200 --> 01:05:08,960
As you're picking up, there's a lot of things that may not make entire entire sense, right?

620
01:05:08,960 --> 01:05:15,560
Because, OK, say you've got it tested. OK, your cannabinoids, they may change over time.

621
01:05:15,560 --> 01:05:20,960
So they're exposed to light photons. I mean, these these compounds must be sensitive to light.

622
01:05:20,960 --> 01:05:25,080
And I know that they are. So exactly.

623
01:05:25,080 --> 01:05:29,280
So perhaps your crops CBTA heavy and it passes.

624
01:05:29,280 --> 01:05:34,600
And then over time, that turns into. So it's.

625
01:05:34,600 --> 01:05:38,960
There's a lot of there's not a lot of nuance taken into consideration in the rules.

626
01:05:38,960 --> 01:05:44,240
It's just the rules are just pretty flat. It's just zero point three percent.

627
01:05:44,240 --> 01:05:47,920
This is how you calculate THC and that's what it is.

628
01:05:47,920 --> 01:05:58,240
So. And that's why and that's why we're why we're here to help is we're just trying to one, just let people know, OK, these are the rules.

629
01:05:58,240 --> 01:06:07,160
And then to provide them with analytics to see, OK, given the rules, how can we say we want to grow hemp?

630
01:06:07,160 --> 01:06:15,320
How can we grow hemp to make sure our probability of growing legal hemp is is high?

631
01:06:15,320 --> 01:06:18,320
You know, just because that reduces your costs.

632
01:06:18,320 --> 01:06:23,560
You know, we're just just trying to help people create value.

633
01:06:23,560 --> 01:06:30,640
Yeah, I'd like to know what sort of data I can get a hold of in my state that's free or anything.

634
01:06:30,640 --> 01:06:33,160
Yeah. So we are medical only.

635
01:06:33,160 --> 01:06:37,160
So we have three certified labs to test cannabis.

636
01:06:37,160 --> 01:06:42,280
So when you whoever that I mean, well, we have quite the monopoly here because we're not fully recreational.

637
01:06:42,280 --> 01:06:50,200
So there are companies that like to apply for their certification and they get it and they get spots number one out of six or whatever it is.

638
01:06:50,200 --> 01:06:55,160
So unfortunately, the monopoly is just getting out of control.

639
01:06:55,160 --> 01:06:58,040
But we our program is pretty decent.

640
01:06:58,040 --> 01:07:04,240
We just the all samples, they go to that certified one of those three testing centers.

641
01:07:04,240 --> 01:07:05,640
And yes, I've applied to those jobs.

642
01:07:05,640 --> 01:07:08,920
So I put a link in last week.

643
01:07:08,920 --> 01:07:11,720
Read me up. I'll send send the link to you.

644
01:07:11,720 --> 01:07:16,840
So there is some Maryland data primarily with sales.

645
01:07:16,840 --> 01:07:18,920
Oh, that would be interesting.

646
01:07:18,920 --> 01:07:21,640
Yeah, so you can at least look at the sales data.

647
01:07:21,640 --> 01:07:30,920
Oh, I'll I'll try again to see if there's anything else I can scrounge up for Maryland.

648
01:07:30,920 --> 01:07:36,360
I think it would just be useful in comparison to is like in a comparison study.

649
01:07:36,360 --> 01:07:43,840
So what I'm curious about what I'm becoming curious about is maybe look at per capita.

650
01:07:43,840 --> 01:07:54,000
So, OK, does maybe Maryland have above below or typical consumption per capita?

651
01:07:54,000 --> 01:07:56,680
So maybe not even.

652
01:07:56,680 --> 01:08:09,760
But what any data points we can get, look at it in comparison to other states and see if there's any any juice we can squeeze from this rock.

653
01:08:09,760 --> 01:08:12,440
Yeah, I'm interested. I like state to state stuff.

654
01:08:12,440 --> 01:08:21,600
You know, people in Maryland are just itching to get their specific strain of flour from a different strain, a different state, even if it means to travel.

655
01:08:21,600 --> 01:08:27,120
So from a customer perspective, our priority is the strain that we like.

656
01:08:27,120 --> 01:08:34,240
That's our medication. I'm not speaking for everyone, but it's it's a general sentiment that people have here.

657
01:08:34,240 --> 01:08:37,560
So I love the state to state combat.

658
01:08:37,560 --> 01:08:43,640
OK, well, that came up early with regulations, so maybe we can try to combine this next week.

659
01:08:43,640 --> 01:08:48,880
May just do a deep dive into regulations state by state.

660
01:08:48,880 --> 01:08:54,120
So just be just OK. Testing regulations. What's going on?

661
01:08:54,120 --> 01:08:59,120
How is it affecting people? So that could be a good topic.

662
01:08:59,120 --> 01:09:06,720
Yeah, or, you know, lack of testing like I'm not saying that, no, we have our stuff is tested.

663
01:09:06,720 --> 01:09:11,000
It's just every now and then we get a little strange QC error.

664
01:09:11,000 --> 01:09:13,280
Oh, yes. And that's what we're talking about.

665
01:09:13,280 --> 01:09:19,880
And that's something I want to leave you today with is be careful what you measure.

666
01:09:19,880 --> 01:09:24,080
Right. Because if you're only measuring.

667
01:09:24,080 --> 01:09:27,440
Acetone or butane, then that's all you're measuring.

668
01:09:27,440 --> 01:09:32,800
And so in Heather's case, then, yeah, it looks like everything's fine.

669
01:09:32,800 --> 01:09:37,400
It looks like pass this big put a big green pass on it.

670
01:09:37,400 --> 01:09:43,000
But you just may not have measured your your silver.

671
01:09:43,000 --> 01:09:50,080
Oh, oh, my God. So so be careful what you measure.

672
01:09:50,080 --> 01:09:52,400
So that's the lesson of the day.

673
01:09:52,400 --> 01:09:56,400
And with that, I want to thank everybody for coming.

674
01:09:56,400 --> 01:09:59,600
Thank you, Melody. Thank you, Charles. Thank you, Heather.

675
01:09:59,600 --> 01:10:00,800
Thank you.

676
01:10:00,800 --> 01:10:04,120
Enjoy your day. Have a productive week now.

677
01:10:04,120 --> 01:10:07,040
Yeah, I'm going to try.

678
01:10:07,040 --> 01:10:11,720
Definitely. Well, until next week, keep your nose to the grindstone.

679
01:10:11,720 --> 01:10:14,320
Right on. Bye now.

680
01:10:14,320 --> 01:10:31,000
Well, thanks again. Bye.