Welcome to the Cannabis Data Science Meetup Group.
I wanted to share with you some of the research and how eager cannabis data scientists like yourself can help out.
I'll go ahead and sort of jump into the material that I wanted to talk about.
But before I do that, why don't I give all of you a chance to talk about what you may want to talk about?
In no particular order, maybe starting in my top corner.
Rick, welcome to the group.
I'd be curious to hear about what you'd like to get out of cannabis data and maybe some interesting projects you're working on and things that you would like to see move forward.
Sure. So, hey everyone, my name is Rick.
I have a background working professionally in big data in the healthcare industry.
So, and I'm also a long time lover of cannabis, whose state has been recreational here for a few years.
So, I've taken advantage of that and began cultivating and most of the passion has gone into breeding.
That is my primary interest in data is compiling as much as I can and creating relations for strains and effects.
And I started looking into it, saw what you guys were doing and it was, you know, right on the same page as what I'm looking into right now.
Ultimately, I'd like to build an app that is geared towards breeders and facility owners that allow them to use their data more efficiently.
I work with some of the facilities in Michigan here and see just all the untapped potential and data going out the window.
So, I'm working on something to fix that here locally.
Simply phenomenal, Rick. Since you weren't here last week, I'll go ahead and recap.
All of the varieties are of quite interest, right?
So, go to any retailer and they're going to be selling by variety.
So, it's a long time tradition in the cannabis industry.
And what we like to do is marry sort of folklore and things that people use every day to statistics.
And so, if people are using these strain names every day, then we're going to see if there's any rhyme or reason to it.
And it seems that, of course, underlying this is the chemical variability.
And where does the chemical variability come from?
Well, that may come from growing conditions, right?
It may come from genetics, could come from probably both.
Love that you're trying to understand this and then you're following it through, right?
From seed to effect, right?
Because, right, not only do you go seed to sale, but what happens after sale, people ingest this and presumably have some sort of effects.
And we're trying to determine what, if any, are those effects.
And we kind of hit on last week that at least I think the appropriate way to go about this is just having just a null hypothesis that,
OK, let's just say that maybe cannabis has no effect on people.
Of course, there's lots of evidence to the contrary.
So, you know, that may be a easily falsifiable hypothesis, but that's a good starting point.
So then, right, you'll want to one by one see what effects these various compounds people are ingesting are having.
At least that's the way I think a nice scientific way to go about this would be.
It's brilliant. You're right on the money here.
This is what people are working on.
And I think you actually have a pretty sophisticated knowledge of this because, as I'll point out to you today,
just say taking into consideration that there are different varieties of cannabis is actually not apparent to many.
And may not be included in the conversation and that has implications.
But I'll point some of that out later today. But enough of me rambling.
Isaac, good to see you today. Curious if you have any projects that you want to see moved forward here in the coming weeks.
Good to see you all again. And I'm Isaac.
I work for MCR Labs located in Massachusetts, cannabis testing lab.
Well, this is actually for both myself and the place I work for.
Kind of my main focus is on lab shopping.
I mean, for the for my employer, definitely as we're trying to do proper science,
we're losing business to those who are fudging numbers.
And for myself, I mean, as a cannabis consumer, this is just MC a lot of health risk and just consumer fraud that is rampant in the industry.
And I feel I'm in a particular position that allow me to do something about it.
So, yeah, my main and I am actually working on fraud detection.
And actually, I'm here today with a question.
I'm hoping to prove to prove your brain to see your thoughts on that.
So it's about using Benfors law.
And I think you have that would assume you have at least heard of it is about the distribution of naturally occurring numbers.
Contrary to intuition is it follows a kind of exponential curve that there is more ones than twos.
And but for fraud detection in cannabis, the numbers, specifically the THC values are all clumped together around 20 percent.
Right. And that's if you go from the textbook kind of manual, that's a no no for application of Benfors law.
But then I think I found a method or operation that is to raise the numbers to a high power, a fourth or fifth power.
By doing that, it seems to be able to stretch the numbers into several magnitudes into a realm where Benfors law apply.
So the most straightforward case is take integers from, say, one to a thousand.
But first, I won't apply it right because it's sequential numbers.
But if you keep those numbers, then first law does appear.
So I'm just wondering the possibility of even stretching the power of Benfors law by allowing the operation of.
Raising the data set to a high power to artificially increase the the magnitude.
So I'm hoping to hear your thoughts on that direction.
And also, in addition, as my colleague, Yasha just joined, we were thinking of another reason that.
Well, as we all know, THC values has been rising for a while.
And the easiest explanation is, well, there could be a lot of explanations.
LAP shopping is definitely one, but there could be also that the girls are getting better at cultivating,
or it could be that they are simply getting better at selecting the best gene to grow.
So we're interested in finding out how much each of those potential causes contribute to the.
Graduate growth of THC content that we've been seeing in the Washington data should show this.
Right. Yasha, what was your input there at the end?
The watch the data that you guys have from Washington should should answer two of those questions,
whether a single. Is it that genetics that yield higher THC are being used more often?
And that leading to average of higher potency or is a single strain tested by the same grower at the same lab growing in potency or staying the same?
I'm going to basically let you in on the golden way that you can actually go about attempting to answer this question.
Right. You came here. So here it is. Right.
Ultimately, the problem you're going to run into is, well, how do you identify which lab results are fraudulent and which ones are not?
Right. And so basically at the end of the day, right, you'd want a zero or one zero, not fraudulent, one fraudulent,
because ultimately you're going to need to predict if the lab results fraudulent or not.
And I think, yes, exactly. You could use Bamford's law to a simple way.
Right. If you built this logic regression, so zero or one trying to predict if it's fraudulent or not, you could say use Bamford's law.
You could you say you could maybe maybe you know this better than I.
I'm just kind of conjecturing here, but maybe as your regressors, you would use things like I don't know how you would incorporate this into your regressors,
but somehow have the number of digits or something of that sort.
Actually, in fact, I don't know how you would build out the regression.
But the first the first and that actually may be sort of the the beauty or not the beauty, but the the novel insight you have.
Right. Maybe if you wanted to patent some fraud detection device, you just have a really good algorithm.
Well, first things first, you need the zeros or ones.
And so now that I've been teasing it, well, how do you actually get about getting that data set?
Well, it so happens that I do believe this data exists and you can get your hands on it in that we've been looking at the Washington State data.
That's because Washington State has a pretty open Freedom of Information Act request.
So you can get these data points specifically and what you'd be looking for or as some of you may have known,
if you look in the news, the former laboratory in Washington State Praxis Laboratory was was called falsifying samples.
And, you know, these lab results were entered into the traceability system, which you can then get those those results.
That would basically be your subset of known fraudulent results.
So that basically you would just say, OK, you know, these are the lab results that we know are fraudulent.
One, the rest of them, you would assume are non fraudulent.
But as you will point out, that's a big assumption, right?
Because we don't actually know for certain t right that none of the other laboratories were not were not falsifying lab results.
So that's sort of a hiccup in your analysis.
But you could potentially just, I guess, look at the Praxis Laboratory lab results.
And so then for that time window, you would just say, OK, these are all the lab results that Praxis Laboratory tested that weren't falsified.
These are the ones that were falsified.
Is there any way to detect to a significant degree of certainty that those were, in fact, falsified?
And that's where your training, your prediction model would come in.
So you say, oh, you know, and that's, I guess, yet another tricky part.
So so that's at least the first tricky part solved.
How do you get your training data set?
The second tricky part is how do you actually predict?
So that's actually going to all have to chew on that.
But maybe you've thought about that.
Any questions at this point?
I'll show you.
There's there's just multiple ways to cheat by a lab.
There's the way that they prepped the sample, just fudging numbers.
And there can be probably four or five different ways by which they can do so.
So we need examples of each to be the ones.
And then a clean data set that's just definitely clean.
Right. And that's a good point.
And so that would essentially be I think we kind of mentioned on this last week where there's so many variables.
Right. And each of these are something that you would control for.
And this is the thing about statistical models is right.
They very laser focused in on the data at hand.
Right. So if you train your model on data and you only had, like you said, the one way that fraud was committed,
you know, committed, then you would miss potentially other ways.
So maybe they are somehow preserving the digits.
So maybe they would somehow preserve Bamford's law.
Something else is going on.
So that's a possibility.
And in fact, that may just be where we just kind of need to expand the data set.
There is the one case in Washington state where I think you could actually get the data.
There have been other cases of fraud, like in Michigan.
I think there was a case that may be debated.
I think that one may be contested.
And then I think there could have been an occurrence in Nevada and perhaps California.
But ultimately, I think what you would want to seek are the results that you know are fraudulent
versus the ones that you suspect aren't and see if there's any systematic difference.
Because otherwise, and this is actually unfortunately what we've kind of seen is basically maybe not wrong,
but I don't think it was fruitful.
But basically, like in Washington state, right, you just see people take simple averages by lab
and just kind of complain that a particular lab may have, you know, high on average results,
which, you know, it's not a good look, right,
if you're producing results that are significantly different than another laboratory.
But that I don't think, right, there's many ways that people can go about explaining that away, right.
There's the, oh, you know, good growers will choose our lab.
As much as it may be like a signal of like, oh, maybe we should look here or look there.
I don't think there is much concrete evidence.
Because the other thing to kind of consider is there's sort of permitted ways that people can kind of push the numbers up.
Or maybe they're not permitted, but they're definitely kind of grade lines.
So, for example, I don't know if this is, this wouldn't really affect total THC or CBD,
but I know there was an effort to just add as many cannabinoids as you can to your panel, right.
Because if you're testing for CBL and CBT and CBCA and CBDV,
so sort of just the more cannabinoids that would just push up the total cannabinoids.
So that was kind of happening. That's kind of the case in California.
So once again, I think there's many factors going on, but that's definitely one of them is
there's definitely been a push to increase the panel, which isn't actually necessarily a bad thing.
But as you pointed out, there may be other things going on.
With that one specifically, so the more cannabinoids you have, the likely the lower THC will be
because it'll get rid of the coelusion problem, especially with Delta-8, for example,
that coeluses with Delta-9.
And in a article about what happened in Washington,
it does reference a price point at which buyers buy cannabis, which is at 20% THC.
And I think THC by most dispensaries is defined as THC plus THCA.
So what they think once is, and I think the number that most people look to is how much THC is in there,
not how many total cannabinoids are in there.
So I think what they're trying to do is specifically how can we increase THC for that final sale to be higher.
I may need some clarification on your second point, but the first point was actually clever
in that you would actually exactly expect as you add more cannabinoids for the THCA
or Delta-8 or so on and so forth to diminish because you can actually see legitimate cases of this.
So for example, in Michigan, we had a lot of PSI labs data.
And you could see back in 2015, 2016, they were just fundamentally testing cannabis differently.
I mean, for example, I believe they were even testing it with GC.
So they started off testing just by GC, right?
Because they were mostly just trying to get total THC, total CBD.
And today, right, you move to HTLC, the numbers are going to be fundamentally different, right?
You would still expect the total THC and CBD to be in the same ballpark.
They're still being measured differently.
And then, of course, the time effects.
So yes, over this, it's real interesting data.
I think I shared it with you last week, but I'll make sure that you have a hold of it because the PSI labs data,
I think, is pretty good.
Do we know for certain that nothing was falsified?
No, we kind of have to go off of heuristics.
And from seeing the scientific director speak and whatnot,
I don't have any reasons to believe they're putting out wrong data.
Their data is interesting to observe over time because you see methods change
and you can observe cultivators in Michigan that appears to be getting better at their craft over time.
Oh, yes. And then you raised the interesting question is, and we could actually look at this.
We could do a regression.
One of our explanatory variables would be number of cannabinoids.
So how many cannabinoids are in your panel?
Is it eight? Is it 12? Is it 16?
And then your variable of interest would say be THCA.
And then you could, I guess, try to try to disentangle that and say,
okay, you know, as we add analytes to our panel, do we detect more or less of these various confidence?
You're smiling. Wait, does this sound silly or?
No, no, no, I think it's perfect so long as the data is clean.
As in if you can take out if you're specifically talking about PSI's data
and we believe that their data is accurate, then yes.
But if we were to look at, let's say, all of Washington data,
then we would first have to remove any data from labs that may not be trusted.
Because that would mess up the whole experiment.
And we may not know necessarily how many cannabinoids each of the labs are offering at each time.
I think that analysis is best lab by lab.
Or you could always basically have a lab specific fixed effect.
I don't know if I've explicitly recommended these before,
but you definitely want to have lab specific fixed effects if you're doing analysis for multiple labs.
So, for example, Rick, when you're doing genetic analysis,
say you want to do some prediction on the effects of your strains or this or that,
if you're getting data from a bunch of different,
you may want to get data from multiple labs.
And then if you do so, you may want to basically control for lab by lab variants.
Back to the topic at hand, I think is, what are all the different ways I guess fraud can be committed?
Or it may not necessarily be fraud.
For example, a consideration that I had, I don't know if this is the most likely,
but it could be a co-founding variant.
And I don't want to throw them under the bus because these are the hardest,
in my opinion, the hardest working people at the laboratory.
Or unfortunately, the analysts may have sort of like a pressure like,
oh, the director wants us to have really high lab results.
And they may just be kind of heavy handed, so to speak, not intentionally,
but just kind of introduce bias of their own because they may just say like,
oh, you know, all my peers, all their measurements are coming out at 30%.
Maybe I'll just be a little basically the way the analysts,
the way that would end up happening is you just be maybe a little heavy handed
on the amount that you sample out and you'd be a little light handed
on basically the amount of methanol you would add in.
I don't think that would be the primary way that fraud would be occurring,
but it would just be kind of something to take into consideration is,
unfortunately, like I said, these are hard working people,
may not be getting paid the most.
And so they may just have sort of this pressure to weigh things heavy, so to speak.
But I don't think that's the, you just think it's just something to consider.
And then, of course, just outright fraud, just outright changing the numbers,
Benford's law, which surprisingly, I never really played around with this before.
So Isaac, if you have any insights or maybe one meetup,
if you want to share any luck or work that you've done with this,
I'd be excited to learn more about this.
The final variant here is just, and in fact,
this is I think what people complain the most or I've heard people complain the most about in Oregon,
is based in, in fact, California recently too,
is basically the cultivator would send in a non-random sample
or potentially even an adulterated sample.
The idea is, say in Washington state, you've got to take a random sample from a five pound block.
And I think they'll, cultivators will even get audited.
So the auditor will come by and say, okay, how do you sample from your SOP?
And they say, oh, you know, we'll just take, you know, five random nugs from this five pound lot.
But it's sort of like, okay, well, you know, how do they sample when the auditor is not there?
You know, are they just picking the choicest buds?
In Oregon, there's been claims that people will like sprinkle teeth or even, I've heard people will even,
and this is where I say, so there's a couple of things people will say, oh, well, they'll paint them.
So they'll like get a cannabis concentrate and, you know, lather that on.
Well, then you run into the situation.
Well, we'll say you've got a cannabis flower with concentrate lathered on it.
Well, a good analyst at the laboratory, you know, they're going to look at this under a microscope to look at it for contaminants or,
you know, foreign matter. They'll see, oh, this has been adulterated.
Well, now this analyst is in, right.
Once again, you've got a person who's getting paid, you know, the bare minimum.
Okay, they see the sample. It's been adulterated.
Well, that's going to be a real part, part, part in the pine because it's really no joking matter,
but they're going to be in a sticky situation because, okay, what do you do?
Do you now say this is actually my recommendation is the lab should then fail that sample for performing matter.
They should say, oh, this sample's been adulterated. We're going to fail this.
Well, now that's now in the traceability system.
The state's going to say you what you did. What, what, what?
Yeah, you know, you adulterated this sample.
The cultivator could potentially be in big trouble.
You know, they may lose their license.
They could easily lose their license over something like that.
What seemed like standard practice from the cultivator, right?
They say, oh, all the other cultivators are adulterating our product.
We'll adulterate it. They may lose their license.
And then the lab, once again, is there in a difficult position because I've heard labs say that,
oh, you know, we're not the potency police.
And I kind of cringed at hearing that because, well, they actually kind of are, right?
Because I was thinking about it the other day and it's there's a reason why laboratories have to get accredited.
And, Yosh, you can probably attest to this.
And, Isaac, this is actually the point you raised at the beginning is it's kind of a bit of a privilege,
so to speak, to be a licensed laboratory.
You know, you're there for the public to say, yeah, so there's a lot of responsibility there.
You know, that's, I think, the reason why it's not cheap to start a laboratory
and the reason why you have to go through these accreditations.
Is because you're the ones who do have to make the tough call at the end of the day and say,
yep, these guys said and send an adulterated sample.
It's tough, but we're going to have to now fail you for foreign matter.
They're probably going to take your license away.
You know, that's just a really, really tough call.
And like I said, they may not lose their license, right?
So they depending on the state, maybe they'll get a warning.
Maybe they'll say, OK, we're going to give you a warning.
It may vary by state.
You know, it's a tough call.
Who's responsible?
But I don't think that's something that the laboratory should just pretend they didn't see.
So that's sort of my take on it.
I suggested this in the past where, OK, we found this big ugly problem.
And instead of just stopping there, let's actually offer a solution.
It's not going to be the best solution in the world, but it can be a starting point.
And from what I've seen, a simple, effective, cost-effective way to go about solving this is just say,
you have to keep the samples for 90 days or you have to keep the sample for 60 days.
This imposes a cost on the labs because they'll have to invest in refrigeration or what have you.
But that's a relatively minor cost than some of the alternatives.
And then the idea is, OK, if there's any question later on down the line,
then you can come back and check the sample.
And basically, if something tests in the store at 40% for flour,
you could just go back to the laboratory and just say, OK, you know, can I in fact see this?
Is it obvious that it was adulterated?
If it's not obvious, then, oh, well, you know, and then or then ask the laboratory to do a confirmation run.
But question before I just keep droning on.
I just want to weigh in. Do you mind if I weigh in on last week's next?
So let's say in Massachusetts, we view pre-rolled joints of flour that is tested for that as flour.
It's not separated by testing category. It is that.
And sometimes the stuff that makes pre-rolled joints is a combination of flour and some concentrate.
And it's submitted to us as flour. And then it's definitely altered.
And to me, from, I guess, the ethical standpoint is.
This is what they submitted. This is what we're testing.
This is the results that we're going to give them.
And it accurately says what's in the product that was given to us
and accurately represents the product that consumers will end up buying.
If that product that consumers end up buying is also.
Given the extra whatever was put into the testing sample.
And that if let's say there's a grower that wants to hack the system, wants to send in a sample that will have more.
That will have concentrate tossed in it just for higher results.
If they do it just once, shame on them. They absolutely shouldn't.
And if the results are ridiculous, then I think a secret shopper program would yield that.
OK, the regulators bought a product, they got it tested and the results differ very much.
Between what consumers buy and what the original results were.
And that should lead to an analysis of the data.
Does it show that this is an outlier? Or is this a consistent thing?
Well, I can surely hear you tell how it is.
Exactly. So I think states just need to be conscious and explicitly say how this needs to be handled.
So, for example, in Washington state, they just say if you add anything to it, it's technically a mixed product.
I don't see anything wrong if you want to call it a mixed product.
But I think the idea behind the testing was whatever you send in should be representative of what you sell.
If you send in flowers sprinkled with teeth on it, as long as that's what the label says
and depending on what state you're in, you may call it a mixed product, then by all means.
I mean, in fact, people have really creative products, right?
There's a famous product, the moon rocks.
And that actually is just exactly that.
Just a cannabis flower coated in oil and rolled around in teeth.
So people will specifically buy those.
But that's fine. But if you're selling moon rocks, send in representative moon rocks to be tested.
I don't think it's 100% okay to just say you're selling flour
because I think the big complaint is people just get low quality flour, sprinkle teeth on it.
What they send in isn't what they're selling.
And then Isaac, question?
Yeah, I just want to kind of put in my two cents on this.
It seems to me that there really is two separate issues.
The first is whether the sample that's tested is representative of whatever is being sold.
And the second being the type of product like a pre-roll, as Yasha was saying.
I mean, cannabis market in general is very new and there's always new products being made.
And we have categorization like flour extracts, concentrates,
but pre-roll is blurring the line between flour and extracts, right?
And that's more of a regulatory problem or categorization than representative.
So I just want to separate those two issues as they are rather than having them mixed together.
100% agreed.
And this is once again, I think we said this last week, but it helps to reiterate this week.
That's why we're talking about these things is it just helps to say them out loud and formalize them.
As I shared this piece of policy with you out of Washington State,
a lot of the language either doesn't take product types into consideration
or when they do, they do it in maybe not the most logical fashion, right?
So just the fact that you pointed out that, oh, there's actually two distinct problems going on here is helpful
because now, right, regulators can take that into consideration that, oh, yes,
we need to be explicit about how are different product types being sampled and tested.
You can say, oh, take a representative batch from five pounds of flour.
Well, then also have sampling rules for joints, have sampling rules for mixed products, concentrate, so on and so forth.
And then also, you may want to the states may want to be explicit about sampling.
You know, what does adulteration, what does that mean?
What are the consequences?
Who is responsible?
Because as I was saying, the labs are in a difficult situation, right?
If all the other, right.
So, for example, Massachusetts, right.
And in fact, I just saw this today.
So, right, if all the other labs in Massachusetts, you all want to be on the same page
because just today I saw that ARM Labs in Colorado has decided to close.
And the reason they stated was they simply couldn't stay competitive
because they think other labs in the state aren't following the rules
and they were doing their best to do testing by the book and they just couldn't stay competitive.
And so, Isaac, you mentioned at the beginning, like, you know, you're trying to solve this problem.
And, you know, I applaud you for doing that.
And it matters.
I do think, you know, MCR labs can be profitable in the long term just doing nice ethical testing.
It's just how do you get through the all this short term noise with, you know,
maybe someone sets up shop in there, they're cutting corners.
So how do you survive the short term?
So I love that you're thinking about this and working on it.
Any more thoughts while I think if I have any more final thoughts real quick?
Isaac, did you happen to bring up the question that was asked on Friday with what model would best,
what can be used for identifying anomalies within these kind of data sets?
No, I haven't asked it.
But yeah, I mean, I very much appreciate if the group have any ideas.
Essentially, we're seeing in Massachusetts data,
there is likely a gap of flowers around with around 18 to 19 percent concentration.
And there's a spike of flowers around 20 percent THC concentration.
As I'm sure we all know, flowers with a threshold, flowers above that sells much better.
And I'm just wondering if you're aware of any statistical method
that is a good measurement of this kind of gap?
This is a phenomenal question.
And perhaps when we'll have to chew on more for next week,
I'll just go ahead and give you my hot take right now
and then think about this more thoroughly over the next week
because this seems like this is a pressing issue.
My hot take is, right, I think you were already on a fruitful approach with Bamford's law.
I would like to just reiterate what I was saying at the beginning,
where I think ultimately to for certain predict fraudulent values,
you'll need ones that you know are fraudulent, right?
Because, well, maybe you won't need them for certain, I guess.
But I think that would be helpful is,
okay, what do fraudulent lab results look like compared to non-fraudulent?
And as I was saying, the data set I think that you can get a hold of
are the practice laboratory results
because you know for certain which ones were falsified
and you can compare those to ones that were probably not falsified.
So you can see if there's any systematic difference there.
And that's where Bamford's law could kick in.
So potentially there are digit differences with the falsified ones.
And then finally, this was something that we had hit on in the past
because Rick, once again, getting back to genetics,
we were actually using a some sort of,
I'm not going to get the name right,
but some sort of multivariate difference in means test.
It was the last episode of statistics we did.
But basically we were trying to say,
okay, there's all these plant patents out there
and the way people are patenting their plants
or one of the ways is by chemical composition.
And so we were saying, okay, you know,
could you actually do a difference in means test?
To basically say, okay, this variety, we've tested it 30 times.
We've tested this variety 30 times.
This one is significantly,
this one's chemical composition is significantly different
than this chemical composition.
And that was how we were recommending people go about patenting their plants
where you would just say, okay, we've tested this plant.
It's statistically different than all the existing patents
and it would make a good patent candidate.
You could potentially do a similar sort of difference in means test with samples.
But I think you'd have to get,
think outside of the box about what means you'd be wanting to compare.
And so this is why I floated last week
that you may want to start looking at ratios.
So for example, we looked at it briefly that, okay,
the ratio between total cannabinoids and total terpenes,
I think we looked at this briefly,
but I wanted to say there was a linear relationship between these two,
which was just kind of interesting.
And like I said, we only have briefly explored this.
You know, maybe there's a sample that basically has a weird total cannabinoid
to total THC ratio.
And maybe it also has a weird beta pining to delimiting ratio.
Maybe you'd have, and I think this is kind of what our friend,
John Abrams over at the CESC uses.
He uses a set of ratios to kind of see if lab results look logical.
Ooh, Rick.
Yeah, sorry, I didn't want to cut you off,
but I wanted to agree with what you were saying in terms of like the lab sampling
and having certain profiles or, you know, like the MIMP profile
and attributing that to a specific terpene
and then that to a specific strain or genetic and be able to track that.
Also with things like hoplite and viral,
it's important now to also for clean cuts
or things coming out of tissue culture labs to establish a chain of custody.
So having a way to do that as well is all interesting.
But right now, none of that data is released for like the hoplite and testing.
I noticed a bunch of startup testing labs
where they can send you at home or they'll have people come on site.
There seems to be kind of a lot of data streams for where that's going.
And so right now it's not readily available or in a database that I'm aware of.
But that's the sort of stuff that interests me.
And then also, you know, if you're working with, you know,
trusted data in terms of testing of those genetics as well,
collecting information from the facilities such as light spectrum,
medium that they use, different types of fertilizers or whatever
and how that may express down the line with different cannabinoid profiles
or, you know, understanding that relation as well or terpene profiles.
I love it, Rick. And you were right at the frontier and you brought up many good points.
And I think what you hit on is what's the starting point?
Get the data, organize it, curate it, and then of course, get around to analyzing it.
You're right on the path, right?
And that is right now there is poor data.
So first things first, get it.
And so I love that. And then you raise issues like, oh, hoplite and viroid.
And this is why I was saying, well, there's a million co-founding effects going on, right?
And so, for example, oh, maybe you're looking at total cannabinoids to terpenes,
and that's a really good ratio. And one sample's off.
Well, maybe we're not taking into consideration hoplite and viroid.
Maybe the viroid interferes with these standard ratios.
So maybe you think, oh, maybe somebody's falsifying results for this one sample.
But, oh, you go take a closer look and it's like, oh, maybe these plants had trace amounts of...
Or maybe these plants were slightly infected with the virus
and they just weren't producing terpenes normally or something of that sort.
Once again, it's all investigation.
That's part of what's interesting about being a data scientist, right?
We wear many hats, right?
As a good researcher, we have to kind of investigate these.
You know, don't jump to conclusions.
But I think we've got some fruitful things to work on.
So, Isaac, I love that you've pointed this problem out,
because as I said, you know, we're all about solutions here.
So let's, you know, put our minds together
and see if we can't think about ways to help the laboratories out.
I think we can use statistics to help.
Well, I think we've got plenty to work on.
So for Rick, we're going to be looking at strain names just to try to find crosses,
because lineage is something that I've been curious about looking at.
Just basically seeing, oh, can we trace all the hazes back?
I'm like working on scraping data from a few different locations.
There's a few sites out there that keep pretty detailed tracking of like a strain and its lineage.
And there's also Reddit, where there's individual subreddits with posts
that you can pull strain names and other stuff out of.
So I'll try to contribute to the GitHub and make sure that anything that I collect will be available there also.
Phenomenal, Rick.
Well, try to come back next week, because genetics and lineages on the agenda,
somehow we got pushed back from today, but definitely next week.
And then we'll also end up looking at consumers in the next week,
because last week we just did a back of the envelope estimate of how many consumers there were in Washington state.
But we can do a much better job of that, given some data that's out there.
So I was thinking, and we can even do some consumer analysis.
So those are the things that are coming up.
We'll get to the bottom of this lab shopping for help Isaac out, help good old NCR labs out.
So we'll help out as much as we can.
Then, of course, genetics and lineage and cannabis consumers.
So those are the topics coming up in the coming weeks.
It's going to be a fun time.
So we'll end the year out strong.
Just like to thank you all for helping advance cannabis science.
Couldn't do it without you, your eyes, your ears, your brilliant minds.
So thank you all for coming together to the Cantlydix Cannabis Data Science Meetup.
So thank you.