All right.
Well, it's good to see everybody today.
So welcome to the Cannabis Data Science Meetup group.
My name is Keegan.
I started a company, Canlytics.
And so I've got a background working in a laboratory and have an economics degree, spent
a bit of time as a data scientist.
So trying to apply that to the cannabis field and try to share what I know and just help
everybody else do cannabis analytics simply and easily.
So just real quick, I guess we can go ahead and go around and just introduce ourselves
real quick.
So if you don't mind just taking 30 seconds real quick.
So I'll just stop starting in the top corner.
Charles, would you mind introducing yourself real quick?
Hi, I'm Charles.
I have 27 years of software development experience and I'm transitioning now into like data science
and machine learning.
Paul.
Sure.
Yeah, my name is Paul.
I'm a data scientist with Gerald Motors.
Been doing analytics work prior for about the last 10 to 12 years.
I'm currently enrolled in a master's program at the University of Wisconsin, a master's
degree in data science.
Currently working on my final project for that program.
And Charles and Keegan have been helping me out with my project.
So welcome everybody.
Awesome.
Megan, it's good to see you here today.
Would you mind introducing yourself?
Hello.
Nice to meet everybody.
I'm Meg.
I've been in data analytics for almost two years.
I was just laid off from Cerner, but I was a business data analyst there.
Awesome.
Well, welcome to the group.
It's always awesome to have more data scientists aboard.
Arunabh, could you introduce yourself real quick if you don't mind?
Sure, sure.
Firstly, glad to meet everyone for the first time.
And yeah, my name is Arunabh.
Basically I'm a data science instructor at Lighthouse Labs, also looking to transition
in the industry for any interesting opportunities.
But yeah, that's what I do.
Basically I've been in the field for four or five years now.
Awesome.
Well, it's great to have you.
Ari.
Hi, I'm Ari.
I've been doing some web development and digital marketing, but recently took a data science
bootcamp with General Assembly and looking to make transition into data science.
Excellent.
The more data scientists, the merrier.
So Gabriel, if you don't mind.
Hi.
Oops.
Sorry, had my webcam covered.
I'm a data analytics student.
I'm doing a bootcamp through Thinkful, so I'm pretty new to all this, but I'm open to
learning as much as I can.
Well, you're in the right place.
So we're all learning here, including myself.
So I've got some to learn today myself.
So we've got Heather.
It's up to you, Heather, but if you want to introduce yourself, you're more than welcome
to.
Hey, everybody.
I'm Heather.
I've been attending these meetings for a couple of weeks now.
I don't have data science, but, or excuse me, data, but I hope to get a job soon where
I'm actually collecting cannabis data and being able to analyze it.
That would be more than exciting.
So this meetup keeps me sane and looking forward to meeting you all.
And how's everybody's 710?
Productive and busy.
So it's awesome to have you, Heather.
Yeah.
The sales on 710 are way better than the 420.
Just letting you all know.
Oh, that's right.
It's oil.
It's a spill pack.
Yes.
So all the RSOs are like missing from the shelf.
People just like take the whole one milliliter and dump it onto like some vanilla wafers.
So you might not see RSOs for a while.
They have to rejuvenate.
Jesus.
Interesting opportunity for some analytics there.
Yeah, I like to do.
So I like tearing into it if I can.
Well, we may have to try to find some daily data and see if we can find a spike there.
So awesome.
Erin, last but not least, would you mind introducing yourself real quick?
Getting a little bit ecstatic.
That's not me.
No, no, no.
Keegan you're muted.
Oh, I was saying, sorry Erin, I couldn't hear you and had to mute you for a second.
But I'll try to re-mute.
Oh, here we go.
Yeah, I'm sorry.
Just getting static.
So we'll have to get your introduction here in a second unless you want to drop your drop
a quick introduction for yourself in the chat and we'll revisit that.
So it's awesome to have everyone here.
So just to go ahead and kick it off.
So each week we sort of look at an interesting data analytics question with the best cannabis
data that we can find.
So we've been primarily looking at lab results.
So we have a data set in Washington state that Charles has been looking at trying to
predict if we can, trying to see if we can predict when a sample may fail quality assurance
testing for say microbial contamination, high residual solvents or potentially foreign matter.
So it's tricky because most samples pass quality assurance screening and only a small number
fail.
So with limited variation it can lead to, it can be hard to fit a good model.
So changing gears slightly, we found an interesting hemp, which is still cannabis, hemp data set
from, let's see, I'm forgetting here the authors.
So it's Midwestern hemp database from the University of Illinois.
So I'll go ahead and share my screen real quick and just go ahead and get everybody
up to speed here.
Just to introduce Aaron real quick.
He's a front end developer, also interested in cannabis data analysis.
And so you're in the right spot as well here.
So I'll share my screen real quick.
So near the end we'll look at, I'll save some time so that way Charles can go over
the work he's done because Charles has done some great work here predicting failures in
Washington state.
So I'll try to leave about 10 minutes or so at the end for Charles to present.
So at least, so we'll try not to get too bogged down in this.
So long story short, we found the Midwestern hemp database.
And so, Philippa Burde is putting together data on hemp strains grown in Illinois, Wisconsin,
Michigan, and Indiana.
I think you can participate in other states.
However, these are the cultivators that are currently participating.
And so we've collected this data last week and have begun analyzing it.
So what are some of the interesting data points we have here?
Well, we have essentially the cultivar, which is the strain of cannabis grown.
The source, we need to pin this down.
It's either the company or the seed company.
I believe it's actually the cultivator.
And then we're interested in a couple variables here.
So we're of course interested in what state it was grown in because we had an interesting
observation last week that the soil breakdown of various states could affect the quality
of hemp grown.
And then we want to also look at the harvest date, if possible.
So this is information about the cultivars, the strains.
And so we ideally want to combine this with data they've provided on lab testing.
So they've sent in each strain to get tested.
And so they received results for CBD, THC, and CBG.
Now I'm going to show you a graph below.
So keep in mind the federal permitted level of THC in hemp is 0.3%.
So if you accidentally grow hemp that's higher than 0.3%, then you need to essentially destroy
your harvest.
So we were talking last week about how it's a tricky dance that cultivators have to dance
where they're trying to maximize the CBD in their products.
However, as we can essentially visually see, there appears to be a positive correlation
between CBD and THC.
They're all cannabinoids deriving from CBG, CBGA to be precise.
So as people try to maximize CBD in their hemp harvests, they run the risk of failing.
So it comes down to the cultivator's risk preferences.
However, there's calculus to be done to centrally try to maximize your CBD while keeping your
probability of failing at a tolerable rate.
So we're going to begin analysis here and see if we can't lend a hand and see if we
can make any sense out of the data provided.
So I have gone ahead and made this analysis available on GitHub.
And I'll go ahead and push the latest commit so that way you can get your hands on the
data.
So essentially, I have copied and pasted this data into just an Excel spreadsheet just so
that we can start to actually read the data.
And so if you have a better way than copying and pasting, then have at it.
However, that just was what I resorted to for the time being.
So we've got the data.
Then the analysis we're doing today will be available for you.
And just to show you where that's located.
So boosting all of the cannabis data science code to a GitHub repository.
That way you can follow along, get the code and follow.
So I must have accidentally saved this in last week's folder.
So I'll fix this after the presentation.
So if you are following along, the script is in the 07.07 folder at the time being.
So long story short, we've got the data.
So the first thing we need to do is look at the data.
So well, simply I'm using Python, but you can use your programming language of choice.
And so just going to...
First things first, we want to just look at the data here.
So I always just look at the first five observations, just as sort of a sanity check to see, okay,
this is the data we have.
And then if you're getting fancy, it's always a good idea just to generate some summary
statistics of the data at hand.
So we're working with about 750 hemp samples.
So these were samples that were sent in by various cultivators and the strains.
So what we can do is we can start adding some variables here.
So the first thing I did was, okay, we can create a variable fail, which is just zero
or one.
And so it will be one if the hemp is higher than 0.3% concentration of THC and then zero
otherwise.
And let's look at that data.
So we can see that about 25% of the samples are failing for high THC.
So we can find that just by taking the mean.
And we were talking about in Washington state how about only we were finding a shockingly
low number, maybe less than 1%.
Correct me if I'm wrong, Charles, but it seemed that it was low.
Percent of samples fall failing for quality assurance reason.
When you have such low variance, you typically have to resort to special models.
So there's a negative binomial model, but in general analysis can be tricky.
So what's interesting about the hemp data is what I would consider a non-negligible
portion of the sample is failing for high THC.
And you have a high standard deviation.
So you've got a lot of variation here.
So it may prove for rich analytics.
So we may be able to help these hemp producers out or labs that are testing hemp.
So this is where we got to last week where I was essentially saying you can find quite
interesting statistics by just taking conditional averages.
So here I'm just taking an average.
So here I'm just taking the mean for each state.
So I'll go ahead and print that out just so that you can see.
So if you do a conditional average, you can see the failure rate varies a little bit from
state to state.
So Illinois has the highest failure rate at above 30% failing for high THC.
And then Wisconsin has the lowest failure rate for high hemp at just over 20%.
So there's slight variation state by state.
However, it'd be interesting to see if there's anything to that.
So for now, I've just taken the conditional average, and there's about a 10% range.
So it may be worth exploring further.
For example, soil.
Next, just wanted to think about whether some of the other data points that we have here.
So if we just print out our columns.
So we have the state.
I think it would be interesting to look at the county to see if you could find county
level soil data.
So there's a lot of factors you could add.
You could do precipitation.
You could be creative.
So I think there's room for analysis there.
Geographic analysis can take a little bit of time.
So just for expediency sake, I began looking at centrally when the hemp was sampled.
So we have the date when the hemp was sampled.
So just looking at the data here, just the way the data was collected.
And this is what you'll learn when you're doing data analytics.
And a lot of you have already learned.
You spend a lot of your time just cleaning up the data.
So from the way I copied and pasted this in, you'll see there's these unnecessary spaces.
The year is in its own column.
So not ideal.
So centrally, these lines here are just turning the date, the sample date, and the sample
year into a nice time stamp.
That way we can work with it as a time series.
But we're not doing time series analysis.
But we can at least have it in daytime.
So once again, make sure to look at the data.
OK.
So what we have here is essentially the earliest sample date was July 27.
And then the latest was November 16.
I should have here we are.
I think if we take the mean of OK.
So I wrote this little function up here.
So basically, the way I'm calculating this is I'm calculating this is the number of days
into the year that sampling occurred.
So for example, the median.
I think we wanted integer.
So perhaps we want strings.
The average harvest occurred on September 19.
So that was when the average harvest occurred.
And then we saw that the latest harvest was in November, the earliest in July.
So there's some variation between when people are harvesting their hemp.
And so essentially, I was wanting to see when people sample their hemp.
So keep in mind, we essentially want to combine some data sets here.
So it would be nice to actually use the harvest date.
However, there's not a one to one relationship here between the agronomic performance and
the cannabinoid data.
So I haven't connected the dots yet.
So I'm using the sample date.
And it's not clear the relation between sample date and harvest date.
So the harvest date seems to be in the future from the sample date.
That's what I had a sneaking suspicion of.
However, it doesn't.
That may just be the situation.
So they may be sampling before they harvest, which would make sense.
So that way, they can just be continuously monitoring their cannabinoids and then harvest
before it's too late.
I think we need to do a deeper dive here into this data to actually understand a bit more
about these data points.
So I'll go ahead and hedge that now that we need to understand sampling date and harvest
date a little better.
However, continuing just to keep in mind, a lot of what I'm doing now is essentially
a demonstration, a crude demonstration of how you can start going about data analytics.
So in practice, you'll want to do things.
You want to research these data points a little better.
Study your models a little better.
Do a bit more background research.
So just keep that in mind.
This is just a quick crude analysis just to give you an idea of what you can do.
Hey, Keegan, a quick question for you on that actual source data.
So I notice it's called hemp data.
I assume it's just another, they're using that term generically as just a cannabis crop,
but are this grown outside?
Are these particular plants grown outside or crops grown outside?
Hemp is typically grown outside.
So typically people just seed acres and acres of hemp.
There's no rules one way or the other.
So you can grow hemp in, say, a greenhouse or potentially indoors.
However, there's a couple of things going on here.
From my understanding, there's essentially three reasons people grow hemp.
I'm sure there's more, but the three main ones are for CBD, so for processing the flower
into CBD.
So there you're going to be growing flower stalks like you see in this picture, and then
you'll want to be harvesting that for CBD.
The second reason is for fiber.
And so then you're going to be growing much more fibrous hemp.
And so there you typically won't flower quite like that, and they'll look a lot more like
stalks.
And then the third reason is for seeds, for hemp seeds.
And so then you'll typically have something that you'd have in this field, but then you'll
have them pollinated.
So you'll have a bunch of male plants in there with the females, and they'll produce seeds.
And then people can either eat hemp seeds or hemp seeds are actually quite valuable
these days.
So that's something that I'm kind of coming to understand is there's such a high demand
for cultivation that the cultivators need to get seeds.
So there's actually it's actually quite profitable to grow seeds is what I am coming to understand.
I gotcha.
Yeah, thanks for explaining that.
I wasn't quite up on the hemp side of the house, so that helps.
Exactly.
So it's a bit more of an agricultural crop.
So it's not the exact same as high THC cannabis.
So is that hence the point three percent or the point three percent threshold for THC?
Point oh three maybe.
It's a point oh three.
It's like nothing.
Yeah, it's point three percent.
So it's it's a shockingly low percent of THC.
However, when you really dive into the science, it is still just the cannabis plant.
So it just some varieties just don't produce much THC.
And so it's real interesting because you'll end.
You know, I worked in a lab that tested hemp products and you'll find farmers who they'll
basically start growing hemp and they think they may have a low THC variety and it may
produce phenomenal CBD, but sometimes it may have like seven percent THC and then they
will have to destroy their harvest.
So it's tricky, right?
Because of course, you know, there are just these strains that definitely don't produce
much THC, but as we can kind of see here, there's.
You know, it's not like they don't produce THC.
So it's basically just people have selected the cannabis for different purposes.
So people selling high THC flour or or oil, they've basically just they just, you know,
selected the plants that have the high THC and they've just bred for high THC.
And that's why you see strains in the stores with, you know, quite high levels of THC,
like above 20 percent THC.
So it's really just what people are selecting for.
And so here people are trying to select like high CBD strains that still don't produce
much THC.
Gotcha.
Makes sense.
So just to kind of wrap this up, what we'll ultimately want to do is somehow analyze the
cultivars, because that's what from my analysis is that's what I try to kind of boil this
down to, because basically I'll get to it at the end.
But I was thinking that, OK, these.
The cultivator is choosing, OK, this is when I harvest, this is when I sample.
But then it may it's in a way, in a way, it's almost the plant deciding, right, because
really the cultivator is just essentially waiting for the plant to finish the flower,
to seed and then they'll they'll they'll harvest it when it's ready.
So the choice is more in choosing the strain, the cultivar, but that has the your ideal
traits.
So choosing a cultivar that that finishes harvest in September.
So it's more the cultivar is finishing in September, whereas some cultivars finish in
November.
But.
But that just threw thought through the time being.
So let's just go ahead and get back to the analysis here.
So just to to walk through just some some basic, some simple regressions just to continue
looking at the data.
Essentially just took a regression of total THC on the days into the year that that the
hemp was sampled.
And so as you would expect, there's a slight positive coefficient.
So the later you wait, the higher the THC is going to be.
And similarly, if you run a regression of total CBD on the days into the year where
sampling occurred, you'll find another positive coefficient.
So this was what we were beginning to talk about last week, where it appears that the
longer into the year you wait to harvest or the longer into the year the plant takes to
harvest, the higher the cannabinoids are going to be.
So we were wondering, does that mean your chance of failing is going to be higher?
So introducing you today, well, a lot of you may have already seen it, is the logistic
regression.
And so I put some some sources here at the top so that way you can just begin.
There's everything from just some tutorials to some originals.
So for example, so if you need some more in-depth reading, there it is for you.
And then essentially, you just need a light tutorial.
There's Geeks for Geeks.
So I've got a couple references here for you to read up on logistic regressions.
So long story short, I wish there was a better picture here.
Well, basically, your regression can take a value of zero or one.
And so they're useful for predicting binary outcomes because you'll basically run a regression
if your estimate is less than 0.5, then your prediction is zero.
And then if your prediction is greater than 0.5, your prediction is one.
So anything you can dichotomize as zero and one, and you have some explanatory variables,
then you can run a logistic regression.
And there's a lot more to it.
So it depends on whether your explanatory variables depends on the choice or on the
individual.
So I'll let you dive into the reading, but I won't bog you down with that right now.
And we'll just get to the analysis.
Long story short, remember, we have coded a failure variable, which is
just zero if it passes.
And so that would be everything below the red line is zero, everything above the red
line is one.
And so basically, we're going to see if we can predict if things, if the hemp sample
fails.
So we've got things coded, and we're just going to use the sample that date.
So does the date, the time into the year when things were sampled, can that be used to predict
when things were, when hemp fails?
So you can basically fit a logistic regression.
Regression doesn't mean too too much, except for if it's positive or negative.
So long story short, let's go ahead and essentially look at the predictions.
The predictions are the most interesting.
Well, that may be my opinion, but it's sort of where the analysis lies with the logistic
regression.
So essentially, using the regression line that we fit, I predicted the probability of
failing for high THC given the days into the year when sampling occurred.
So keep in mind, so here's another plot, which basically adds the, what we observed.
And so I'll be frank, it's not really the best fit.
So when you see in the examples and tutorials out there, logistic regressions, you'll see
much better fits than this.
So graphically, it doesn't look like the best fit, but you can essentially start to see,
keep in mind this is a crude analysis, but essentially what the prediction is, is the
later into the year that you wait, the chance of failing for high THC increases.
So all the way, so this is why I have this function right here.
Oh, well, I guess I've got this plotted down here.
It looks like all the way through September or so, you only have maybe less than a 25%
chance of failing.
And then it looks like, as you wait, your chances of failing increases.
So I wanted to quantify this a little bit better.
So using just another statistical package.
So here I use stats models, and then here I used scikit-learn.
So once again, it's just a package.
In fact, I actually have an implementation right here that if you want to see how to
write a logistic regression with just matrices, then have at it.
So long story short, I'm just using tools here.
Essentially trying to just quantify this regression to see, well, how accurate was it?
So apparently it predicted with 76% accuracy, but I just kind of want to show the breakdown
of that.
And so here we're predicting failures, and it's actually a failure.
So that happened 16 times.
Then here we predicted a failure, and it's actually a pass.
That's 12 times.
Here we predicted a pass, and it actually failed 168 times.
And I'm going to revisit.
And actually, I'll visit that now.
So essentially, that's worrisome.
So in the Geeks for Geeks article, maybe I'll pin it again.
They start to talk about essentially what do you want to optimize for in your logistic
regression?
So are you trying to get things precise, or are you trying to make sure that you don't
miss any?
And so from the laboratory's point of view, you don't want to be predicting a lot of passes
that are actually failures.
So in my book, it would be like this is the number that you would like to minimize.
I think it's OK if you predict failures and there are actually passes.
So I think this quadrant is not the worst in the world from the laboratory's point of
view.
But these are basic.
Anything in this quadrant is essentially a missed opportunity.
And the reason I say missed opportunity is so remember, so there's 750 samples here.
So it's all about the value of time.
So it takes about 10 minutes, give or take, to analyze one sample of hemp.
So that's 125 hours to run 750 samples.
And there'll probably be pauses in between when an analyst has to load on some more vials,
because typically an instrument may only hold 50 to 100 vials that are being analyzed.
So it may seem like a short amount of time, but it's a long amount of time.
And there are a couple of laboratories doing these tests, but essentially you're trying
to identify the failures as soon as possible, because the sooner you can identify the failure,
you can notify the cultivator, and then they can begin their remedy process.
So that's almost five days.
So the idea is if you can load the failures onto the instrument first, then you can notify
those cultivators almost five days sooner than you could otherwise.
And that could mean a lot of money.
So basically, let's say they just tested one field and it's high for THC.
Well, maybe they have some other fields and they need to harvest those like right this
second before it's too late.
And so say if they waited five days, well now all of their fields may be at high THC.
So it may seem like trivial or small or inconsequential.
However, from my book, basically identifying these failures as soon as possible could just
generate enormous savings later on.
And it's just one of those things where just these small little ways that you do things.
So the small little ways that you do analysis can have these enormous consequences.
So even if this model may not look the best, it's only predicting 16 failures out of 750
or so.
Those are still 16 failures that you can load on the instrument first and just identify
them that much sooner.
And so long story short is what I would try to do is essentially build logit models to
try to, of course, you want to maximize these two quadrants where you're predicting failures
correctly and predicting passes correctly.
However, I would continue to work on the model to try to reduce the number in this third
quadrant and increase the number here or in the second and fourth quadrants.
So that's how I would approach this analysis.
So Keegan, question for you, make sure I'm tracking to this.
So essentially by trying to predict which of the samples are more likely to fail, they
kind of get bumped to the front of the line whenever they come into the lab.
And by doing that and giving them first priority, you're giving that customer potentially more
time to react to their failure.
Exactly.
And so there may be better ways to do it because I was thinking once it's failed, it may be
too late.
So it may be better to try to find things that are right at the brink.
So right at like 0.28, 0.29.
I don't know how you would go about doing that.
So from the lab's point of view, it's always sort of a delicate, it's always delicate to
just tell a cultivator, you've actually failed quality assurance testing or you've failed
hemp testing.
And it's just part of the business.
It's just necessary.
So it's often just a little bit delicate.
So the sooner they know, they can go ahead and let them know the news.
Like you've got high THC in your crop.
And like I said, it's just that much sooner that they can begin essentially the remediation
process for their next crop or adjacent crops.
So this is something that, like I said, the hemp producers, they are, it is a problem
for them at the moment.
So there's a non-negligible portion failing and it's something that people are trying
to pin down.
And so from my point of view, even if their crop fails, but they have five additional
days to try to work on it for next year, something's better than nothing.
So I think that could go a long way.
So that's essentially where I see the gains being from the laboratory point of view, from
the cultivators point of view, these statistics may be a bit more interesting.
So seeing, okay, we may want to, right, because different cultivators have a different risk
preference, right, risk tolerance.
So this is essentially your probability of failing.
So some cultivators may have a higher preference for risk and they may want to push it out
a ways to try to, right, because they're maximizing their CBD.
And let's say they get revenue proportional to the CBD that they produce, right?
So it's just a cost benefit analysis.
So the longer you wait, the higher your benefit is, your CBD, but the higher your cost is,
which is your probability of failing.
So depending on their risk preference, you would just, one could think, well, simplifying
things, you know, if these were just the only variables at hand, then you could just make
a trade off.
Okay, you know, how long do I want to wait for the amount of CBD given my probability
of failing?
And so then it would just be a simple cost benefit analysis, and you could use that to
just choose, I want to harvest on this date.
In reality, were you going to say something?
Oh, I'm sorry, Keegan, go ahead.
Or I was just going to say, in reality, there's a lot more factors.
But do you have a comment?
I did regarding your CBD to THC correlation plot.
There was one outlier that was very interesting, which is right there on the right hand side
where they're budding right up against the 0.3% THC threshold, but they're maximizing
their CBD.
That guy right there, to me, when I see that, that to me is like suspect.
They either cracked the nut or something else is going on.
At the 0.29%?
Yeah, and they were all the way up there to almost 15, 14 and a half CBD.
Yes.
And yeah, that's, you're right.
So they could have, they may have just gotten lucky.
I don't think there's anything shady going on because we've got some pretty reputable
labs here.
So one would like to think that it's just luck of the draw.
Yeah, maybe I'm being too cynical.
But like I said, I mean, I think it's just an outlier.
Like you said, anything's possible.
I think maybe if you saw like a string of observations all at 0.29% that may be suspect.
Yeah, and also the point by 10% CBD, which is kind of has low THC.
So if it's right over there, right there.
Yeah, that guy's doing pretty well, right?
He's got a very low risk threshold.
You know, if we're using the terms that you kind of defined through your logic model,
he's got low risk and kind of high reward there.
So that's a pretty good performance.
And this is actually essentially what cultivators are trying to figure out.
And like I said, it just luck of the draw getting these right.
So it's basically, you're trying to figure out these, the right, the right strain.
So you're trying to figure out the perfect strains to grow.
And so basically, they're still figuring it out.
So for example, these, the front range biosciences, they may want to start breeding hybrid number
five.
Right.
And so I think essentially, I think this gets back to sort of essentially what I'm starting
to narrow down on is it's about select.
There's a lot of factors going on, but a lot comes down to selecting the right strain.
So and there's, you know, and so that's why it's in high demand.
Right.
So just because you have hemp seeds doesn't necessarily mean they're going to be profitable.
So you know, a lot of people out there, they may not know one way or the other, and they
just purchase a lot of, you know, berry blossom seeds or presidential seeds.
And they just, they just got, you know, the wrong genetics.
And so I think this is what hemp producers are starting to learn is, you know, the strain
you're growing really matters.
So I think that's, I think that's what I'm starting to get to is because here we're starting
to see that, okay, when you sample, it starts to matter.
And so I'm starting to think, okay, that may be dictated by which strain you're actually
growing.
Were there multiple, multiple incidents at the same strain just being harvested at different
times?
Oh, yes.
And so there is room for strain analysis here, because as you see, we've got a lot of repeats.
So here are some of like the popular ones.
So like, it looks like, like this cherry wine just looks to be, you know, unbelievably popular.
But you know, it may not really be, you know, the best choice.
So, so, or maybe it's a fantastic choice.
And so I think, I think there's room there to be done, because basically, let's say you're
a new, a new hemp producer out there.
Well, you know, you're, you've got your, you've invested all this in your cultivation equipment,
and you're about to buy, say, 30,000 seeds to go seed acres.
Well, you want to make sure you pick out the right variety.
And I'm sure every seed producer out there that you call is going to say that, oh, yeah,
their seeds are fantastic.
So so I think that's, that's where I think our analysis comes in, is, you know, if you're
a hemp producer, you can begin to start tailoring our analysis and see if you can't, you know,
start picking the varieties that are right for you.
So it's known that over time, THC increases in these in hemp.
So and there are a lot of cases of farmers harvesting, and it being tested like a month
or two later, it passed when they harvested, but it fails later.
So there's sort of this race against the clock.
And I'm not sure I guess I'm from this data, it looks like CDB also goes up over time.
But it's kind of trying to balance the two out.
Yeah, definitely a trade off like you're saying, Keung, like a cost function here.
Exactly, and so I think I think there's room for deeper economic analysis.
And I think I think what Paul, I think you, you're you're real good at noting these outliers
and their significance, because I think essentially, that's what you're you're aiming for.
Because if you can find a stream like this, then you don't have quite the the the careful
dance that you have to dance, right?
Because you're growing something right here that it's going to be just such a such a fine
line between passing and failing that, you know, that that could pose a that, you know,
the probability of failing could pose such a high cost on a cultivator that it wouldn't
be worth it or that they could.
It's not uncommon that I mean, they could end up as a loss, right?
Because if you act, it's not unheard of to, you know, to grow a lot of hemp, and then
it fail.
And then you'll you're in the red.
Yeah, yeah, very risky.
In in in from firsthand experience, a lot of hemp producers don't understand that risk
getting into it.
They think that they're growing a hemp strain, and they just they're just there's not going
to be much THC in it.
But as we're seeing, you know, there's there is a positive core, it appears to be a positive
correlation here.
So it's just that some of them, it's just a weaker, you know, weaker correlation.
So yeah, good, good work, Keegan.
And Charles, that's good stuff.
The data that was collected and consolidated, the gentleman that you mentioned at the beginning
of the presentation, did he reach out to each of the states separately to get this information?
Yes, so the Washington State data is just from Washington State.
So so in fact, touching on that real quick, didn't leave Charles 10 minutes, but I'll
give you 10 minutes at the start of next meetup.
So essentially, what Paul is referring to here is in you can find this in in our prior
in our earlier meetups, there's several links here, essentially to a Washington State database.
And so this data was collected by Jim McCray, who's a researcher in Washington State and
shared with me.
And then Charles has done some analysis here, just trying to predict the probability of
a sample failing.
And so Charles, I apologize to you that I didn't leave you 10 minutes.
And so if you want to go over this at the beginning of next week, we can set that aside.
Okay, that's cool.
All right.
And then for the rest of the crew, you can look through Charles's work here, because
he's done some fantastic work, essentially seeing if we can, similarly to what we've
done today, predict the probability of failing, given what sample type it is.
But I'll let Charles get more into that next week.
All right.
Looking forward to that one, Charles.
That looks good.
All right.
Well, I'm going to end the presentation here.
And so real quick, are there any quick comments, questions, concerns before we call it a day?
Nope.
Well, it's been awesome having everybody.
It's great having some new faces here.
So it's awesome seeing you, Gabrielle, Eri, Megan, and of course, Paul, Charles, and Heather.
So it's been fun.
So let's keep in touch, keep bouncing ideas off each other.
And then next week, we'll start with Charles's analysis and keep doing analytics.
Sounds good.
All right, everyone.
Have a productive week and until next week, keep being awesome.
Bye now.
Bye-bye.
Bye.