Welcome to the Cannabis Data Science Meetup Group.
As always, you're in for a treat today.
We're going to crunch a lot of cool new cannabis data.
Finally got our paws on the Washington State data for 2022.
This is spanning, I believe, January 22nd or so of 2021 through the end of November,
the beginning of December of 2022.
We have almost a year worth of lab results.
I'll share these with you.
I've been curating them.
There's maybe between 48 and 52,000 lab tests, lab samples.
That's almost 2 million analytes that were tested.
Really exciting, one of the largest sets of lab results, and it's in complete population,
which makes it real fruitful for analysis.
Instead of having a potentially non-random sample, we have each and every lab test that
was conducted in Washington State, which gives us a lot of statistical power.
Without further ado, I'll go ahead and just give you a brief presentation of what I'm
interested in researching.
As we may know, survey data may not be the most reliable data in the world because it
relies on people to self-report.
There's a lot of literature in psychology and in economics that suggests that people
may not necessarily have the incentive to be honest with what they tell you.
What they teach you in economics is look at people's actions rather than what they may
say.
People's actions hopefully will reveal their preferences.
In this case, we're interested in many things.
One of the things we started talking about in the past few weeks were contaminants.
We started looking at microbes.
In the past, we've looked at residual solvents.
Now that we have 2022 data, pesticide testing was mandated in Washington State in April
of 2022.
Before that, other states have pesticide testing mandated, California, Oklahoma, perhaps I
want to say Massachusetts.
Most states already had pesticide testing mandated, but as we know, we don't have the
best access to data in these states.
For example, in California, the best we know of what pesticides are cultivators using is
this self-reported survey.
As we can see here, there's only 59 growers, so this leaves us wanting for more information.
This was four years ago.
It's been some time now.
We've got some new data and some new techniques.
Let's see if we can't replicate this per se, but let's see if we can't come close to
replicating an analysis of pesticides being used in cannabis.
As always, at the Cannabis Data Science Meetup Group, we're here to get our hands on the
actual data.
As I said, I was curating these data points for a cannabis data project.
This is open source, and you can get your hands on this too.
There's one thing that I want to iron out, and then I'll get this sent to you.
Essentially, what I want to iron out, when I first did the first pass, my first pass
just curated data from January to the end of November.
It only curated around 48,000 unique inventory items that were tested.
In the second pass, I collected all the lab results from January to December 7th, and
that puts us around 52,000 lab results.
It's not 100%.
The samples don't overlap perfectly, so I want to rerun this and get a better merge
of this data for you.
However, right now, we do have more than 48,000 inventory items that were tested, so this
can get us a good start at the very least.
First things first, let's look at this data.
What do we have here?
These are the different types of products that were tested in Washington State in 2022.
As we can see, flower lots were the bulk of testing, with just shy of 25,000 lots of cannabis
flower being tested.
Second, hydrocarbon concentrates.
This appears to be the most popular type of concentrates, with more than 6,000 inventory
items tested.
Surprisingly, edibles were higher on this list than I anticipated.
As you can see, there's different types of products for sure.
However, for today, I thought maybe we could just look at the bulk, the flower lots, and
the hydrocarbon concentrates, just to pick two categories, just to keep things relatively
simple.
This can be generalized to more sample types.
This is a nice starting point.
What are we after?
What does this data even look like?
I'll just show you a random sample here, just to show you all the cool data points we have.
There's a lot of data for each of these.
I'll just show you some of the cool things that we know.
Also, I may have mislabeled this as the retailer.
This may actually be the producer.
I want to go through and double-check this.
We know the name.
This was cherry sorbet.
We know the inventory type.
This is cherry sorbet flower.
This one coincidentally failed lab testing.
We've got the results here.
We should be able to dig in to the results to see why it failed testing.
This one may have failed for microbes because there were no pesticides or residual solvents
detected.
Long story short, we've got some nicely formatted data.
We'll just keep it visual.
I'll just show you real quick the various pesticides that were detected in cannabis
flower in Washington state in 2022.
As you can see, piperanol butoxide takes the lead with more than 600 detections.
Pesticides are notoriously hard to pronounce.
As I mentioned in prior weeks, peculiar names, there may be more magic to the show than one
may think.
For example, I've heard a chemist make the point that one of the reasons why these chemicals
may have odd names are for comprehension and memory retention.
It's going to be hard for you to recognize, say, and remember imidacloripid.
I probably mispronounced that.
Just to show you a bit about these pesticides here, I thought I had one of them pulled up.
Here's piperanol butoxide.
This was what I was searching for in the prior week.
This I think is, what's the name of this?
A molecular structure.
This is a way that you can depict chemical compounds.
As I was telling you, I'm doing my best to read up on organic chemistry and probably
know less than your average high school chemistry student.
From what I'm gathering, this is just a simplified diagram.
This is organic chemistry.
The fundamental building block of organic chemistry is carbon.
This is what life forms are made out of.
It's real interesting how life works because you just have these fundamental building blocks.
You've got carbon, which are essentially depicted from my understanding by these various lines.
I'm going to butcher the interpretation of this, but from my understanding, these lines
represent carbon bonding to oxygen.
Instead of having carbon bonding to hydrogen or oxygen.
Like I said, I'm butchering this.
I'm not going to be able to think of this on the fly, but here you can see the white
molecules, I forget if it's hydrogen or oxygen, aren't depicted.
It must be hydrogen, I think.
Yes, because see here at the end, this is H3.
So three white balls and one gray ball.
So I think that's three hydrogens and a carbon.
And then the red must be the oxygen.
So here you can see the red depicted as O's.
So long story short, you've got these organic compounds and they come in various degrees
of complexity and add a few more hydrogen molecules or carbon molecules and these can
turn into two different substances.
So I think that's also part of the magic that's happening behind the curtain in that people
may develop slightly different pesticides.
So they may develop a pesticide that's similar to trifloxistrobin, but it's slightly different.
They'll give it a different chemical name.
Say trifloxistrobin's regulated, the new molecule is not.
It can be a way to still use pesticides while avoiding detection.
So I've heard pesticide screening be described as a cat and mouse gang where they, and it's
not just cannabis cultivators.
This is, I think, could be an issue in agriculture in general.
People engaged in agriculture, we've talked about this in the past, contaminants are of
top concern.
So they're always chasing new innovative ways to fight off pests and disease and molds and
fungi.
And so they're coming up with new chemicals.
These chemicals are then being studied by toxicologists and then regulators will put
limits on these and then say they'll be screened for in cannabis and then people will start
using a new substance if they're failing a lot.
So this is just something to watch for.
Okay, so we haven't done too much yet except just describe the data.
And that's often a useful first step.
For example, this data, it's all publicly available and sitting right there in front
of us.
However, it took a good bit of curation simply to be able to count the number of pesticides
that were detected.
Well, not only can we count them, but we also know what type of product.
So right now this isn't differentiating between say flour and hydrocarbon concentrates.
Well, I'm going to show you a nifty way that you can model this data.
Okay, so we've talked about how cultivators may want to view the risk, the chance of failing
as a business risk and to factor that in.
So for example, if you're cultivating flour and you value your flour at, I forget what
the going rate is, but it's $1,000 a pound or $2,000 a pound.
So that's your value of a pound of flour.
I think they changed the flour lot sizes.
And in fact, I saved them, but the old ones were a five pound lot.
And so I think they changed them.
I think some lots are up to 50 pounds now.
But let's say you've got a five pound lot.
Each pound will be parsimonious and say each pound is valued to you at $1,000.
So a five pound lot has a value to your cultivator of $5,000.
Okay.
Well, if we were just going to do a simple count here, we know, let's just do a back
of the envelope here real quick, right?
So there's 600 give or take.
Actually, let's find the exact number just to pesticides and perennial butoxide.
Okay.
644.
Okay.
Well, there's only, well, not only there's 48,000 observations and 644 of them failed
for piperennial butoxide.
So if you were just going to do a simple back of the envelope calculation, you could just
say, oh, well, I know how many failed for piperennial butoxide.
I know the total amount tested.
Well, that's around 1.3% that are failing for piperennial butoxide.
So if you have a $5,000 value flower lot and your risk is $66.
So that's a $66 cost, expected cost of pesticide screening.
And so $66 out of 5,000, right?
We backed out the failure rate there.
So pesticide screening imposes around a 1.3% cost.
This is an unconditional cost.
We haven't conditioned it on anything.
We haven't conditioned on the product type.
We have conditioned on where the grower may be located.
So this is completely unconditional.
The cultivator has an unconditional risk of 1.3% if they're growing a $5,000 lot.
That's a $66 cost.
It may not seem large, but margins are tight in the cannabis industry.
And this adds up.
So for example, if all of a sudden maybe you're selling not just $5,000 worth of product,
but maybe you're selling $500,000 worth of product here, well, all of a sudden, now that's
a $6,500 cost.
$6,500.
That's non-negligible.
So if, say, your revenue is $500,000, it's a decent sized cost.
And if all of a sudden you're faced with this cost and it's going to be lumpy, so that's
assuming this is all evened out.
Well, this is going to be a lumpy cost in that you may not fail pesticide screening
for a long time, and then one month you fail and you have to now bear that entire cost
in that one month.
So this is what I'm trying to drive home in that just want to bear in mind that there
is a cost.
And you can minimize this cost.
So for example, this is unconditional.
Maybe we can find some conditions that may lower your probability of failing pesticide
testing.
And maybe you can manage those conditions.
In economics, we learn that profit maximization is equivalent to cost minimization.
And that's what I often try to stress to people in business.
So many times people are worried about maximizing their profit, just trying to get as most money,
as most revenue as possible.
And they don't take into consideration their costs.
And I can see people run awry with this, run astray with this.
And an effective way to achieve the same result would just be to have a laser focus on keeping
your costs to a minimum while engaging in as much business as you can.
Cool.
So I have droned on enough about that.
Let's get back to some charts and actually some cool statistics here.
So now I'm going to introduce conditional statistics.
And these are the two things I tell you with, right?
Start with simple statistics.
So these are counts.
These are actually conditional counts.
These are counts by analyte.
So those are conditional statistics.
A count, a total is a statistic.
A count by an analyte is a conditional statistic.
They're amazingly powerful, interesting, and simple.
And then you can just keep adding conditions.
Well, as I alluded to earlier, perhaps product type matters.
Well, how are we going to model this?
The way I've encouraged people to go about modeling simple data in the past is binary.
Zero, one.
This is really going back to the basics in statistics.
I'll be loading all the binary statistics episodes shortly.
So those will be coming.
This is just a clever way of modeling the data that opens it up for clever statistical
exercises.
OK.
So what are we looking at here?
So we're seeing that almost a little less than 48,000 of these samples had no piperanial
butoxide detected.
And a little more than something went awry in my counts here.
But I'm just going to power on.
And I want you to encourage you to come back through and double check my work like a good
scientist would.
And so 600 of these are coded as one, meaning that piperanial butoxide was detected.
Fantastic.
Well, when you have simple binary data, zero or one, you can estimate a probit model.
And this would basically predict the probability of being zero versus the probability of being
one conditional on your explanatory variables.
This is exactly what we want.
We want to know the probability that piperanial butoxide will be detected conditional on the
sample type.
We already know that the unconditional probability of piperanial butoxide being detected is around
1.3%.
Well, what's the probability in flour?
What's the probability in hydrocarbon concentrates?
You could do this again by just taking another conditional count.
I'm simply introducing the probit model to you as a new...
Well, we've already discovered this tool, but it's another tool that you can use to
calculate conditional probabilities.
And it's a tool that we can load up with explanatory variables.
And I will save the big takeaway for the end of the day.
You'll tease that the magic is going to happen with those explanatory variables.
Now, I'm isolating a subsample here.
So essentially, just want to look at flour lots and hydrocarbon concentrates.
Why?
Just to keep things simple.
There's no reason why we couldn't look at more inventory types.
These were just the two most frequent inventory types.
And so just wanted to use them for a demonstration.
Okay, enough of me rambling on.
Let's estimate this model.
So it's real simple to do.
Well, and this is actually why I encourage you, if you really want to cut your teeth
programming and learn statistics as intimately as possible, then I would challenge you to
code a probit model.
Because here we're just using one from the stats model package.
And we can do it in one line of code.
That's phenomenal, because now we can add this to our favorite programming algorithms
and we could calculate thousands upon thousands of probit models in a short fashion.
But this is a useful exercise.
You'll have to hit, say Wikipedia or your favorite statistics textbook.
And it's a non-trivial task, but it's completely in the realm of possible.
And if you're up for the challenge, it's more of an exercise, right?
Because at the end of the day, I would recommend to use the stats models, probit model, because
one thing I love about open source is when it's used by many, many thousands and thousands,
perhaps millions of people, then it becomes robust.
And perhaps little quirks get discovered that you couldn't figure out by yourself.
So probably the stats model's probit model would probably be more robust than any probit
model that you may be able to write.
You could probably write a better one, I'm sure.
But as I said, it's a good exercise in both programming and statistics.
And you can then double check it.
You can basically double check that the probit model you wrote is equivalent.
So that's just a tangent on programming.
Here it is.
And I'll just talk about the model and then show you the actual takeaway.
So when you estimate a probit model, OK, so we've got our 30,000 observations.
Our dependent variable, remember, that is going to be a binary, 0 or 1, mostly zeros.
And in fact, that's actually a problem.
And there may be better models out there.
That's often a criticism in that if your zeros and ones aren't balanced, then that can lead
to issues.
But this can at least give us a good start.
OK, so what do we have here?
Well, we've got various coefficients here.
And don't read too much into the coefficients alone, because the probit model, you actually
have to evaluate it.
The one thing that you can see is that this has a positive sign on it.
So right out of the gate, we're thinking, OK, hydrocarbons may actually be detecting.
We may be detecting piperannual butoxide more often in hydrocarbon concentrates.
OK, well, how much?
Well, guess what?
It's actually super easy.
So the way you go about this is you find what's called marginal probability of detecting for
one category, and then you would do it for the other.
And if these weren't categorical variables, then you would use the mean.
So you would say, oh, what would be the probability of, say, your regressor was number of plants.
What would be the probability of failing for piperannual butoxide given the, say, average
number of plants?
And so you can find different marginal probabilities.
OK, so enough of me talking.
Let's look at the numbers here.
And we've got, OK, what's the marginal probability of flour?
Well, here, I'll just plot all of this out, because this way, we'll have a nice visualization
while I'm talking about it.
So all I did here was evaluate the model at the mean of each of the groups.
So here, flour, we've just got a constant.
And hydrocarbon concentrates is always 0.
That's a dummy variable.
And then, of course, hydrocarbon concentrates has a constant.
And hydrocarbon concentrates is always 1.
So really simple to calculate the marginal probabilities for dummy variables.
As I said, it's a bit more complicated if you're, say, you want to find the average
marginal probability.
Or I may be butchering my language here.
But if you want to find the marginal probability of a continuous variable, I want to say you've
used the mean.
So let's just go ahead and interpret the results here, because I think this is an interesting
takeaway that's non-obvious.
If you talk to a chemist, kind of wanted to bounce this off of our good friends over at
MCR Labs to see if this may be in line with what they've observed.
So if any of you want to reach out to MCR Labs and see, hey, is this what you've observed
or something similar with piperanial butoxide in cannabis?
Because I think this is an interesting insight that is non-obvious and requires a good bit
of data curation as well as clever conditional statistics to get to.
And I think these are the types of insights that people in the cannabis industry could
really benefit from, and they may be completely oblivious to the fact that they can get their
hands on these statistics.
So what is this?
So this is now the conditional probability of piperanial butoxide being detected in your
cannabis flower.
Ooh, I also want to walk back that cost I was talking about earlier.
This is just the probability of piperanial butoxide being detected.
You may not necessarily fail quality assurance just because it's detected.
So depending on what the cultivator's internal quality control is and the state limits, they
may not actually face the 1.3% risk of losing the batch outright.
But it at least gets you thinking in this ballpark.
So this is sort of my main insight for today that, as I said, this may be apparent to chemists
in the laboratory but may not be obvious to other people.
Piperanial butoxide is detected at a higher rate in hydrocarbon concentrates than in flower
lots.
Well, why is this?
One thing that's happening is, well, in hydrocarbon concentrates, yes, you are concentrating THC
in other cannabinoids.
Well, if you look at, and this is why the chemistry is mighty, mightily important.
Look at this, the boiling point of piperanial butoxide is 356 degrees Fahrenheit.
What is the boiling point of tetrahydrocannabinoid acid?
I want to say we can find the boiling point, 180 degrees C.
Okay, yes.
So check this out.
So THC boils at 155 degrees Celsius.
So that's the point where THC turns into vapor.
Well, if you're trying to keep THC as a liquid, a hydrocarbon concentrate, then you'd want
to keep the temperature less than that when you're going through extraction.
Well, if the temperature is less than 160 degrees Celsius at all times, piperanial butoxide
does not boil off.
So that means in this process, the idea is other compounds are being boiled off.
So compounds you don't want, let's say you raise the temperature to 150 degrees Celsius.
Well, a lot of the plant matter, I think, is going to start boiling off.
You're going to have other solvents boil off.
Well, guess what?
Your piperanial butoxide will remain.
And so what ends up happening is not only are you concentrating THC into hydrocarbon
concentrates, you're also concentrating any high boiling point pesticides that may be
in the product.
So this may be a reason that concentrates have a higher detection rate.
Another reason is perhaps flour that is more contaminated is more likely to be processed
into concentrates than flour that's not.
There's a lot going on here.
However, I just wanted to point this out that if you are, say, a processor, then you may
want to be a bit more concerned about piperanial butoxide detection than a cultivator.
And just keep this in mind as you're factoring out your costs.
As we pointed out, it's not the greatest cost in the world, but this is getting up there.
This is now around, let's say you have to destroy your batch if it's detected, because
maybe that's just your internal policy.
Well, now you're facing that around 2% of the time.
That's going to pose a non-negligible cost at that stage.
And so just something to think about.
And then basically, what's my big takeaway from this?
This was really just an exercise, because as I said, you can reach out to MCR labs or
your favorite chemists in the cannabis space, and chances are they'll be able to tell you,
oh, yes, hydrocarbon concentrates.
Yes, we detect pesticides at higher rates than in flour.
Yes, piperanial butoxide.
It may or may not be the most detected in other states, but chances are they may have
seen that.
So this may not be anything new under the sun, but this is a new tool that you can use.
It's called the Pesticide Study for predicting the probability of pesticide failure.
And that is exactly what I've been wanting to study for years.
So probably since about 2018 or so, the same time this study came out, and it may not be
2018, I've been definitely interested in what pesticides are being used in cannabis,
what's the detection rate, what are the amounts being detected, and what are the factors that
are causing pesticide contamination.
And a hypothesis of mine that I've wanted to explore since 2018, but we just haven't
had the data, is proximity to say agriculture or industry a potential factor in failing
for pesticide testing?
We have heard about pesticide drip in the past, and something that I'm interested in
is let's say, is your risk of failing pesticides location dependent?
So depending on where cultivators are in the state, or some of them at higher risk than
others for pesticide screening.
And so some of the conditions I wanted to look at were say, proximity to a dairy farm,
or various types of farms.
So people farm cherries and apples and all sorts of different commodities in Washington
State.
And what you could do is you could calculate those data points.
So we know where the cultivators are with latitude and longitude, and then you could
calculate the distance to say, the nearest dairy farm, or the nearest road, the nearest
metropolitan area, and just try to look at all the different geographical factors that
may be at play.
And many of that may be insignificant.
There may be no significant relationship on failure rate.
But as I said, we're looking for something obvious here.
And this piggybacks on the lesson of last week.
When you're working with statistics, differences should hopefully be readily apparent pretty
quickly out of the gate.
And so here, pretty quickly out of the gate, we see, OK, hydrocarbon concentrates, flower
lots.
It looks like there's something structurally different about pesticide detection in these
two.
Well, now is the fun, because now we get to hunt for other explanatory variables that
may be significant.
And this is how you could potentially get a paper published, which would be quite an
interesting feat.
So I think last year, there was thousands of papers written on cannabis.
And that shows you there's, one, a lot of interest and demand for this type of study.
So perhaps if you think of a novel topic, then this is something that you could write
about and that people could study.
Or if you don't want to research, maybe you can think of a business model or a business
endeavor to profit off of this information.
So say you do figure out some structural risk for failing for pesticides.
Well, maybe you could reach out to all the cultivators who are at high risk and just
let them know.
So you say, oh, maybe being close to a dairy farm puts you at high risk of failing for
pesticide X.
Well, you could reach out to all the cultivators near the dairy farm and just let them know,
like, hey, you may want to be on the lookout for this pesticide.
That's maybe not the best business endeavor ever, but I'll let you brainstorm and think
of them.
But long story short, that's what I have been wanting to study, as I said, for almost four
years now.
What are the geographic factors that may affect pesticide detection or failing for pesticides?
There may be none, right?
That's really a good null hypothesis, right?
So the null hypothesis would be no geographic forces matter, right?
Being close to a dairy farm doesn't matter, being close to a metropolitan area doesn't
matter.
So we would start with the null hypothesis of no effect and then see if we can't find
evidence that any of these places may have a statistical effect on, say, failing for
pesticides.
So hopefully you found that interesting.
I'm not sure if this is of widespread interest to people, but this was something that has
been on my mind for a handful of years.
And now we finally have the data that we can actually answer this question.
So here's a start.
We looked at one factor sample type.
Well, now let's expand this, right?
And that's the nice thing about the ProBit model.
We can just pack in explanatory variables.
So now is the fun time, right?
And so this is, as I said, this is how statisticians have a fun conversation at lunch.
You just start talking about, oh, you know, what explanatory variables may matter and
do we have data on those?
And if we don't have data on those, how can we proxy those or how can we measure that
or how can we measure this?
So now that you've got a good research question, you can start to brainstorm all the factors
that are important.
Go ahead and conclude it there.
I feel like I'm rambling for the amount of material that we've covered.
The goal, or not the goal, the insight of the day that I thought was fruitful.
So last week's insight, I think, was helpful, but maybe not the most motivational ever.
But as far as statistics goes, it was important.
Well, this insight of the day is super motivational, and that is persist.
Just keep trying.
And it's going to be surprising how long some things take before they pay off.
And what am I talking about?
Well, I want to share with you some big news.
So as you know, Candletics writes open source software.
One of the tools that we've built was a open source software development kit for integrating
with the metric API.
And metric is the cannabis traceability software that is used in the vast majority of states
that have legalized cannabis.
And a long time ago, many moons ago, in 2021, so this was in March of 2021, I wrote this
metric software development kit and got Candletics verified in a handful of states to interface
with metric.
Well, it takes a while for things to proliferate on the internet.
And lately, the metric software development kit has been getting a lot of interest.
And finally, Candletics has contracted to integrate a company with the metrics API.
And this is really exciting, big news.
One, just personally, it's a new contract.
But two, this is a demonstration of this open source software being used in the wild.
And I've teased a lot that, yes, I think this is software that you can use to make money.
And I think I'm finally demonstrating that.
So the contract is specifically just to help the person use this software.
And this is one thing I learned from someone that if you show someone how to do something
difficult, then often they'll pay you to do it.
And so that's one of the reasons that we put out this software is we think this is a useful
tool.
We're happy to show people how to do it.
And then if they need help using it, we're more than happy to help.
So this was sort of big news I wanted to share with you and just let you know that, hey,
this metric software development kit is actually quite useful on making money off of it.
Hopefully this company gets value out of it.
And as I was talking about with the Probit model earlier, the more and more people that
use it, the more robust it becomes.
So I would actually benefit if you use the open source software to make money, because
hopefully if you use it and other companies use it, hopefully the software becomes more
and more robust.
Maybe you find something that needs to be ironed out and you can or cannot share if
you wish.
But if you decide to share, then hopefully I can improve the system.
Hopefully that'll make your life better.
Hopefully it'll make my life better.
And we can just keep going back and forth.
Just incremental progress, win-win, and value for value.
It's taken a long time to get to this stage.
And I want to thank you all for your support to get here.
Every dollar mattered.
I couldn't be here without literally each and every penny.
Each and every penny got put to unbelievably good use.
I want to thank you.
Thank you for your eyes, your ears, your eyeballs contributing to CanLinux and the Canis Data
Science Meetup Group.
Because of you, we've covered an enormous amount of ground and I think are really starting
to reap the benefits of the open source software and really start to provide people with value.
So just want to give you that little bit of motivation that if you don't at first succeed,
try, try, and try again.
Also, I know it was me standing on the pedestal for most of the time today.
So hopefully you at least found it interesting.
Just in the last couple of minutes, do you have any thoughts, comments, questions, Candice,
before we call it a start of the day?
That is just fabulous.
Fabulous presentation and coding.
And that's interesting too, CanLinux is interfacing with the metric API.
And Massachusetts uses this metric and also too, I do believe that MCR Labs, Yasha and
Isaac will be very interested in really how you nail down that concentrates can also have
concentrates of pesticides.
That's concerning.
As someone that did a lot of RSO in the beginning, I think that might fall under that product
type category.
And those are patients that are doing that RSO and we don't want high pesticides.
So amazing Keegan, you're always uncovering just good stuff.
So thank you for everything.
I love your enthusiasm, Candice, and just to continue piggybacking on that, yes, CanLinux
actually is verified with metric in Massachusetts.
So that demonstrates that the software can be used in Massachusetts.
And as you pointed out, this analysis hopefully will be quite valuable to people because personally,
I just try to limit my consumption of foreign chemicals that I don't understand.
So for me, everything else held constant, I would actually prefer the flower with no
pesticides detected versus one with hyperannual butoxide detected, even if it is below the
limit for the state.
And so this is why I argue that if people work to make their lab results accessible
to people, then this could actually be a selling point for you.
People are always saying, what can we compete on besides THC concentration?
Well, guess what?
If there were two products, one was 18% THC, the other was 20% THC, and one clearly had
on the label no pesticides detected, and the other one, hyperannual butoxide, was detected,
it would be a no-brainer for me which one I would choose.
This is a personal preference.
Everybody has different preferences.
If you're looking for a factor to compete on, then as a cultivator, you may want to really
stress the fact if you don't have any pesticides detected in the flower that, yes, there's
no pesticides detected in this sample or in this flower, and other cultivators may not
necessarily be able to make that claim because as we were showing today, there's a non-negligible,
and that was just one pesticide.
There's other pesticides too.
So a non-negligible amount are getting detections even if they may be below the limit.
So say you're a concentrate provider, this could be a really big selling point for you.
Well, also too, maybe you want to look for pesticides with a lower boiling point than
THC, right?
And just to get it chemically out of the product, right?
Even though people might still want full disclosure that maybe no pesticides are used, but never
mind.
Well, you're thinking like a chemist, and one would hope that the processors, and I've
met some really, really, really smart processors.
I imagine a lot of them are taking this into consideration.
However, we've talked in the past about asymmetric information exists, and we can't pretend it
doesn't.
So there may just be processors who aren't aware that boiling points of different pesticides
matter.
So one thing is just helping people access the information.
So just do they even have the information to start with?
That's a good piece.
And then actionable insights.
So we're working on those.
We don't have too much that you can act on yet except taking into consideration the risk
of flour versus concentrate.
But I think there's a lot of doors that have been opened simply by creating this data.
So now you can take this data, put it in a database, and do a quick query.
So now all of a sudden, if you see a product, you can now know if there were any pesticides
or say residual solvents detected.
So I think this could be something that could help out consumers, could help out regulators,
help out the labs, help out the cultivators and processors themselves, could even potentially
help out retailers.
Maybe retailers don't even know to look for this.
So that's what we're all about.
And that's what we love to do is find useful cannabis analytics that can help as many people
as possible in the cannabis industry or the cannabis space.
And as I said, this was a small start, but we're all about one molecule by a time.
So literally today, we looked at yet another molecule, piperanial butoxide.
And I think we moved the bowl forward, even if it was just one molecule.
So I'm happy with today.
So thank you.
Thank you.
Thank you for coming.
Keep advancing cannabis data science.