Sure to carbon on earth is in many different reservoirs.
On the top we've got the atmosphere.
One of quite a bit of worry, of course, so there's in total around 750 Giga tone.
And that carbon is recycled quite quickly it's it's.
Yeah, it's cycles around within years or so, then you've also got about similar amount of carbon stored in plants for example, trees, that will live up to a few hundred years.
But many of those just live one year, then below that we've got the soils and Pete deposits quite a large amount of carbon in there and they're that carbon is stored in.
So away from the atmosphere for up to say 10,000 years or so, yeah.
Then of course we've got the fossil fuels, which have been away from the atmosphere for millions of years are essentially ready carbon free, and are now being pushed into the atmosphere.
Then, about half of the carbon is actually in the ocean.
Yeah. So, in the surface ocean, there is around 1000 Giga tons of carbon. And that gets recycled. And so through communication with the atmosphere for outgassing and things on scale over on forums hundred to 1000 years, then below that we've got the deep
is even further, isolated from the atmosphere. And that has say throughput time or second time around 2000 to 2000 years and it depends a bit on where you are on Earth.
And so, and so from the pre course survey that there's at least a few people are looking at Ocean sentiment course, so damn marine ready carbon is quite important.
And as bit of an issue in that say on average, you're ready to carbon age from any marine material will be too old by, on average, around 500 520 years or so, but it's not a constant average just changes, it can change over time.
And also certainly will also change depending on the location where you are on Earth, because there's places where old water that has been away from the atmosphere for thousands of years, is going through upwelling, and then causing carbon to be admitted,
which is relatively relatively less ready car running this.
And for example, so one region where there's lots of old carbon barriers lots of dilution of carbon is around the Antarctic the Southern Ocean.
And if you are if you have material from there.
The way.
Sorry. Oh,
sorry.
No worries.
So, animals, organisms that live around the Southern Ocean and eat carbon from there. And if you would catch them now they would really carbonate them they would show quite an old age, for example, during the Scott boater expedition.
They got some hundred some animals and if you read a carbon date that penguin that I found apparent age is 1,390% 40, and a seal is about the same age.
So there's this additional local, regional reservoir effects and decision has to do quite a bit with the global conveyor belts were around green limbs and quite fresh water is pushed downwards and has been transferred across the earth, and over scale
of 5000 2000 years.
And when. During that time, they rated carbon in those waters, when us away from the ocean and build us away from the atmosphere, it will then decay, gradually, some parts of Oceans they will be quite an age offsets.
Yeah, we have to be aware of that and have to correct for that.
And you can do that using for example the marine database which is hosted in Belfast by polar Reimer and Ron Reimer and and what it does, it has lots and lots of data points of shells, that are found on the beach, for example.
So quite recent sales or sales different collected for them by museums, media, and before the 1950s, so before this, the boundaries.
So we have shells have known more or less known tender age, and we measured already carbonation that gives me an idea of at certain locations, what is local.
Additional retroviral effect is.
So you can click for example, on one countries I Northern Ireland here if you happen to be here.
There will be a few data points of shells collected. And that will tell you them say some estimates of this additional local restaurant effects.
Again this this can change over time as well.
But yeah, that's just something that you have to take into account, and will also increase your uncertainty.
Right.
Not only marine dates have this thing of have a reservoir effect, but also legs, certainly also in legs that these ready carbon offsets can certainly be there and they can also vary a lot over the core depth.
It's not only in legs that are in regions where desert covenant bedrock the bedrock, but can also be other ways for old carbon to end up into the lake, big problem.
I've worked on several lakes, where sometimes the.
Then, this is this age offsets sometimes four or five years and then 3000 and then it says can can vary quite considerably. And it's not always clear what the reason is behind these.
These offsets and their changes, but could be in some cases you could have a lake where there's cold soil around it, and that's occasionally, washed in, and then as carbon that is say a few hundred years old, into the lake.
Understand perhaps them sentiments, or the could be other reasons.
Could revoke access to or other reasons. Yeah, so do not think that because if you're like, has no carbonates then already carbon date should be fine.
There could be many other reasons whether or not fine.
And you choke. If you do work on Lake sediments. You also need to check for the correctness of your settlement. If you're using Belk settlement dates.
You can use all you have to check how reliable they are, for example by measuring the surface sediments, if you know that it is from a recent ages.
If you don't measure it and get 1000 year old age, that you know you have to apply around 2000 years angel said.
You can also use pair dates where you have a single depth, you measure both a Belk date, and for example Poland, or charcoal or non ready current date for example you can use different layers of known tender age or lead to 10 dating, and then have some
dog settlement dating as well, to see what is offset could be and how it can change over time.
Yeah.
All right.
No, he also look at whether the target event is data is one look too much into this, but one issue is that if you have material.
For example, a piece of junk have said sediment and you want to get a date for it.
I don't know how much you would generally pay for single dates, but will be around.
$400 a selfie four or $500.
So, you don't really often want to spend money, lots of money to do replicate dating, but that's just the types of things that step radiocarbon labs do by sending around material of exactly well material, one piece of material, and then chop it up in
to say 100, different samples and send them all across the road to different radiocarbon dabs, and then let them dated and report vector results.
And that's done for here for a piece of boot, which is Denver data so we know what the xxH should be.
But we don't tell that to the right of Kyra maps. And then over the cabinets measure sample gets an age report to age.
In this case here, we've got the standard would which is then returned the age I returned. You can see that no, not a single lab agrees on the age. Yeah.
Each level reports, slightly different mean and slightly different. aero bars. And there's also I think three or four naps that don't even fit on this graph, they're not plotted.
Yeah. Some labs, give very very high proceeding dates and others much much larger uncertainties.
So, one message here is that you shouldn't go to different labs. Of course you get scattered, but that's not really the message I want to get across because I think it is, it is important to go to different labs, see that the best methods are comparable
and the results are comparable was also even if you stick, stay within the same lab, and measure the same material several times, which is what we do in Belfast we have several standards that, that have we have made once, and that we keep on ready carbon
dating in order to have some standards in our data and process to make sure that the machine is is not drifting away.
Yeah. So even for the standard there will be quite a some some change over time. Yeah.
So, each time you send in material for dating, you get a number, and it's a very expensive number, but only a number it's only estimates, and next time you'll send the same material you might get a different age.
So there's quite a bit different.
So we just saw this date comes with a lab error. And we should not neglect that. So it's just an estimate, it's an expensive estimate. And, yeah, it's not a perfect age.
Certainly not.
And if we don't think about possible source of contamination, especially for older ages contamination with modern carbon can be a disaster.
So imagine that you have a if you have like a piece of sediment, for example, we had a real age is a 50,000 years old, and the real already carbon ages 1500 years old, but you're working on the sample, and by accident, a little hair or a flake of skin
falls into your sample, and you don't see it happening.
And it's contaminates your sample with 1%.
Modern carbon. Yeah, so 99% of your sample is of the age of the rate of carbon is of the he wants to be. But you have contamination with 1%. Modern carbon, then your parents age on this access will be around 3334 or five years old.
Yeah.
So this huge impacts of tiny amounts invisible amounts of contamination can have enormous impacts, especially on all the dates. And if you think back to that animation with all the little yellow dots that disappear over time, men were at ages of four
yeah beyond 3040 50,000 years ago, there's so few ready government items left, that if you add a contemplation just a few radio carbon atoms, your sample will be much wrong.
Yeah.
And if you have so 1% on termination you get say are you real he gets off by several 10s of thousands years, if you have say 10% condemnation which is still, what I will be hopefully visible, you would end up with ages, less than 20,000 years old.
Now imagine that
there's a quick question in the chat about whether there is partitioning or fraction ation of carbon isotope in charcoal between what's been burned off and what's left behind.
Or in general how that works.
Yes, there is.
Say, so you have see 12 and 13 and see 14 and there will be a bit of.
Whenever there's any chemical reaction happening. This will happen to bit slower for heavier atoms.
So, you will have some, some biases happening that way. But the good thing is that we measure not only see 14 but also see 13 nc 12, and any friction nation that will happen will happen, about twice as much for see 14 didn't foresee 13.
That's why we also measure c 13 and correct for that bias, so that that's discrimination. So, if we measure how much discrimination has happened against see 14, you can then estimate how much it should it should have been for see 14, so that we do correct
for that.
I hope that answers the question.
Now I mentioned that you have material, which is contaminated by 1% personal always 1%, it's 1% plus or minus something. Yeah, sometimes it's point 8% in another sample, maybe 1.2% so this source of contamination is not constant changes bits randomly,
over time, but always is, on average around 1% and dusty black dots here.
Yeah. So, if you have 1% contamination but that's better perfect rating amount in absolute terms it doesn't really do much to dates that are say quite reasons, but to work all the dates, it's kind of have a huge impact on these days.
So yeah, this is something that's is causing me lots of trouble at the moment.
But yeah, so it's optional thing to to consider, especially when you're looking at all the dates, but also this impacts can have large yeah it can have large impact, even on the relative dates, if that makes sense.
Yeah.
If you have contamination with all the carbon then that's less of an issue, but especially, all of a sudden modern carbon can be problematic.
Right, let's move on to calibrate to calibration again. So, if we just open our again. We have already loaded.
The Intel, our package. And that comes to the function called calibrate originally that was in the clam package but now it's in the Install button so if we simply go to the terminal and type calibrate, And then, open bracket close bracket.
Then
microphone open baby crying.
Thank you.
This is calibrating a standards of day to day life and not break arm date of 2450, plus or minus 50.
And I like that a lot because that's where the calibration curve here in green, the integral 20 curve has a plateau.
It has to be a period in time when in Europe at least as lots of things happening in archaeology. So there's lots of radiocarbon dates that span, that are from around that time.
It's also it's a plateau so if you have a date of 2450 uncultivated hears it could actually stemmed from a period of a fee and a half centuries yourself.
At least if you have single dates. If you start to include more information multiple dates and some deposition models we can actually use these vehicles to get very precise ages, but for single dates, this can be really problematic.
Yeah.
So what we see here in this graph is in red, we've got the date of the data, we want to calibrate.
Then we've got this
Goshen shape the bell shaped curve on this on this axis which is the uncultivated era distribution which we think that's if we have a date of two 450 plus minus 50, you can assume that it will then be distributed as normal distribution.
Then we use the calibration curve, which is green one to translate the radiocarbon years into calendar year, because that's what we're interested in. Yeah, we were not interested in carbon years.
He wants to know what the calendar years our of our samples. Yeah.
So, generally, what is generally used is that we assume that, under rated garbage kill our samples are distributed, like a normal distribution.
Actually, bacon uses student t distribution which is very similar to normal distribution, but has slightly wider tails, so it can accommodate more scatter will come to that later.
Yeah. So, yeah, could you return to that previous graph for just a second.
This one, and I know, yeah I know you're going to talk about this a little bit more but I wanted to point out that the, that probability distribution function, the sort of the access for it is invisible, it's, it's not shown on there.
But what that is a probability of that date that radiocarbon date, being each of those calendar ages.
Yeah, that's it. This is very confusing at first but I think this is if you get this then you will understand a lot more about radiocarbon calibration, so stay tuned.
Yeah.
Indeed, so you're right stairs is very confusing because what we see here actually not to access but as for access in this graph, two of which are invisible.
Yeah. So here we've got the distribution, the probability distribution which is going this way. And it's plotted on an invisible, additional access.
Yeah, so we should not interpret this thing in terms of these have this access. It's an invisible, it has an invisible additional access here for uncultivated date.
And similarly, the calibrated probability distribution has additional uncluttered access, which is not related to the red ready carbon age here.
Yeah.
What it is is simply this graph so you've got your measurement here, and we assume it is normally distributors and given this measurement, we can ask for any value for example 2200, how likely that value is given our date, even our measurement.
And if you're far away from the measurement, it's very unlikely that those values belong to that measurement. But as you climb the hill. We reach very likely value so if you will between 2400 to 500 those values are very likely given this date yet so
So we're very high up to possibilities to distribution.
So this is a very important concept, and we generally the axis here, we generally don't really got it.
All we know a bunch of know is that this entire area seems to one, and the value here isn't really that important doesn't really tell us that much, but so when we got when we got to calibrate dates then, yeah, distributions are plotted on invisible additional
access.
Yeah.
Alright, so let's go back to our then we're going to put this code here the blue code into our.
So, If we want to look at normal distribution.
We can ask for any value, we can ask how likely that value is for assuming a normal distribution. For example, if our measurement is 130 plus minus 20, we can ask how likely the value of 125 years.
Yeah, so we can ask what is the normal distribution, the norm, open bracket of devalue 125.
Given that the measurement is one of the 30 plus or minus 20.
So we can ask are so assuming this distribution, how, what is the likelihood of probability of the actual value being one of them 25.
And it tells us that's CHEER UP 101933341.
So, 1.9% chance, probability of one of them 25 being the actual year of the updates or the actual value of that measurement.
So quite low. Yeah.
But we can also just return back to the original command we can also ask how likely is then the value of one of the 30.
Given that the measurement is 130 plus or minus 20.
And so about savings, but 19.9% now sorry, 1.99%. So 2% probability. Yeah, so the individual values, still have very low value of, very low probabilities, but that's because this is a quite a wide uncertainty took 20 years lab and 30 translates to say
100 years show total distribution. So what we can do is we can go and ask what the probability is not for one valuable for a whole range of values, for example, we can get a new parameter, or variable called x course, why not.
And we assigned.
Do a little arrow thing again. And we give it up the numbers, zero to 250.
Right.
So then you have a parameter, or variable x. And if it just asked for x is just gives us the numbers.
Zero to 250.
Okay.
And now we can ask, we can say, No, give me the probability for each of those values given a measurement of 120. So we can say x.
And then we go back to 1030, plus minus 20.
And then you get.
I lost like 250 values for the probability distributions Yeah, for, for those x values.
And we can actually better to debt as another variable called props for abilities.
So probabilities.
We that new variable be assigned the value of the norm x, comma, 130, comma, 20.
Now we can combine the two. The mean bind them into columns, very first MX at the first column. And then we have a second column.
And then we get a list of two columns there for each of the values for x, we've got the corresponding probability.
Yeah.
And so each of those values are very unlikely, but if we actually combine all the props together, all those 250 values.
To add up to one.
Yeah, so this whole area for distribution as up to one. So, we're still good.
Now we can plot these guys as well we can say, simply say plot breakdown bracket, then x on the x axis and probes on the y axis, and a close bracket.
And that gives us this horrible plus with all those little dots, because by default, are will clubs on our values as dots and not in a line.
So we can go back and tell us to have a type, it should use line graph a line graph.
So now we have for each of the values, zero to 250. We have to probability going up to 2% or so, and receive a normal distribution.
Yeah, so we can see each value has a certain probability for that measurement. Yeah, so that measurement is only measurement and we can then ask, What if the value was really hundred.
Or what if it was really 200, we can calculate the probability. Yeah.
Okay.
Good.
All right.
You can also use the same approach to make a very rudimentary calibration.
By invoking the coloration curve. So we have the coloration curve, which you can see by Mike making a variable called cc innovation curve.
And again we assign something to it.
And for that we we assign the function calibration curve shi shi shi curve, which by default, puts income 20 into that variable.
So we now have variable cc which contains the whole calibration curve.
So if you type CC, I guess.
Nine 9501 lines pros, each of which has at first element, it has the calendar year.
The second element is to radiocarbon year, and the third element is to uncertainty the error of deaths integral ready carbon here.
Okay.
Right, so our calendar years here in Chicago is the first column of this, of this CC. And we can do that by saying CC.
And then we use square brackets, which just something about which column, we want to take. Yeah. So we say, cc square brackets comma one.
And then we close the square bracket records that will assign the first column of this variable called cc into this new variable year dot info.
And then we could do the same for radiocarbon years blowing to those Canada years to see 14 in Cal, that becomes CC. But then the second column. So the second column is the column for it already covering two years of the coloration curve.
And now we can use those columns that variable dental calculates probability for each date for each year. So, we can go again, calculate or make a variable called props be assigned to it.
The US again, the normal distribution, the norm.
And we want to have the rate of carbon years as the values that want to test, see 14 dot info.
And then we add the mean and standard deviation of the value of the measurement of interest, which is one on a 30 plus or minus 20.
Yeah, so we have probes. as a variable which contains lots and lots of values.
Yeah.
9501 values.
Now we can then drops.
On the exhale uploads the years of Intel.
And then a lot of props
and
and devil gators this horrible graph, which goes all the way back to 55,000 years ago and has this really sharp peak, which doesn't really make much sense.
So what we'll do is we'll just adapt that access limits.
So tell are the limits of the x axis should be between two values. So the minimum to left most value and the right most value.
The minimum will be Jarrow the most recent age.
And we have 300 Esther last age.
And we also make it a nice line graph instead of these dots.
Okay.
And now, we have the calibrated distribution for the last few hundred years for that date 130% times 20.
cc you don't need software, dedicated software to get your calibrated data you can actually calculate on yourself.
Luckily enough we don't need to do that because there's software to work for you, for you. But this shows you yeah how, what's the process of this collaboration.
Okay, so if you're still awake, less than look at how we go about calibrating a date. Yeah. So again, let's go back to this.
Imagine that we have a date of 2450, and not worry, this is 2,400% minus 50.
And then in green, you've got coloration curve. Okay.
Again, we're assuming based on this measurements that we that this behaves as a normal distribution so that uncertainty is distributed distributed as normal distribution.
And his normal distribution is placed is plotted on an additional access, or just drawn a line here. Yeah.
Now, what we want to do is we have to compare.
And we have to somehow combine
this uncultivated unknown date with the coloration curve to get an idea of what are likely calendar ages for this date, because we're not interested in reading garden aged or interested in Canada ages.
So what we do is we go to any single calendar here, for example, 2150.
And then, shall we go to that calendar year. And then we check for Intel we go to the Intel curve and me check what the corresponding radiocarbon years for the coloration curve.
So that's some, some value somewhere.
And then we draw horizontal line to see for depths given radiocarbon year of the coloration curve, how far away it is from our measurement.
And this case for 2001 of the 50 Cal dp the curse funding integral radiocarbon year is very far away from our measurement, in effect, it's so far away that we're actually in the valley of this whole probability mountain.
So we have a zero probability that x yo radiocarbon age of that measurement is more than 2200. So what we do with them is transfer the height of that floods, to our calendar axis, and then we plotted on invisible, additional access.
So in this case, you've got zero here.
Yeah.
Oh, that was a lot of work just for one camera here so we can do that automatically, of course, so we can simply go for one camera year, check the corresponding radiocarbon year, check if Hydra for religious devotion and draw that Providence distribution
on the x axis, and then repeat for next year, go for it ready carbon year, find how far away is from the dates plotted to probability, etc.
And in disguise who that 50 years spacing to for now, but we do that, no spacing. Soon, is perishing, or more close.
Yeah.
So now we're getting it can be used, which have ready carbon years of the congressional offices are just getting a bit likely. Yeah. And so we get a bit higher up the coloration probability curve and upload that heights.
So we get a bit higher up the coloration probability curve, and the club that heights. On the kind of the scale.
Now we're getting us coming years which has very likely ready carbon years.
Given the date.
So we can very likely value so we've got that on our calendar scale this invisible extra access.
Yeah. So if it were to be all together in one room we would actually start drawing this on a piece of paper, and it's it's really confusing to try to really understand how this calibrated distribution is is made by pushing the date, through the calibration
curve.
The important bit is that we go from calendar years and for each calendar year, we asked, What's the corresponding interval, see 14 year and how likely is that see 14 year given dirty carbon dated for wanting to calibrate.
Yeah.
So we can do that a bit faster by calculating simply every time of the year. And now you can see how over time. So we're still it's very unlikely coming years, but we're reaching territory.
You're getting higher I'm going to religious tuition.
Staying quite high, but me getting a bit little bit lower, getting away from the ready carbon date.
Then we get back, way back, and a way for always.
Yeah. So that's how we get this weird asymmetric multimodal distribution, the calibrated distribution by pushing this radiocarbon date and there's no one has to achieve through the collaboration curve.
Yeah.
So, yeah, again if we just go back to our and type calibrate calibrate.
You get this similar similar type of radiocarbon date. It's a slightly different data.
And again, we're using the calibration curve and the coloration curve is simply
era, or a table of three values of importance, Cal dp so he kind of years, the corresponding radiocarbon years, and then the air traffic type that GC becomes a calibration curve.
And then if we just want to show the first few lines of deaths.
That's variable UFC here that's four zero call dp number of corresponding ready to carbon years 199 plus minus 11 for year one LBP, it is 197 plus 11 for you too.
It's fun and 511, etc etc.
Okay.
Right. So that's how you get those weird kind of ratings distributions.
And we can also assume, instead of using the normal distribution we can use one student t, which is very similar to this normal distribution. But when you use for collaboration it actually results in slightly wider tails.
So it really helps if we have some scattering dates, and then it will actually results in some areas which are still possible back to normal distribution already says like notice it's not possible.
So, by using a student to distribution which actually makes a lot of sense given the amount of scatter that we see in real data carbonates using the student to distribution we can get actually quite, quite a good models and deal with scatter.
Alright. Just one more thing in that often you see calibrated dates reported as that calibrated ranges.
And what that is, it is the highest posterior density ranges.
And they're conceptually made by lowering horizontal line and seeing lowering it until you reach a certain a certain amount of the curve that of the entire distribution that is covered by the gray areas.
As you know, the line further and further more and more of the area, crushed by deadline is covered. So in this case here 68% is covered, which is one standard deviation.
But nowadays most often we go to 95%, standard deviation ranges.
And then your reports, those ranges Yeah, this one, this one, this one and that one.
My street please an indigenous Yes. What does HPD stand for something probability distribution highest posterior density is posterior, not even close.
austere density. Because there's many ways in which you can find in Michigan, clubs, and 95% range of such a beard horrible distribution. You could use.
So you could eat of a two and a half percent of each side. But then you might not capture the highest piece. So, this value, this measure meant, or this way of calculating the highest posterior density ranges and shores, that the most likely bits of your
calculator distribution are included it favors cakes.
Yeah.
Okay. But that's actually just for reporting your dates and dose ranges are not used in any way in the calibration or in age modeling software course it uses the entire distribution not just a single, single ranges that just for us humans to more or less
make sense of the date but does not really used into software.
Okay. And so there's also this thing of position versus Chrissy Metz you could have.
If you have samples which are very much in one area. That's nice, because they're very precise, but doesn't necessarily mean that they're also accurate.
Yeah.
You want them to be like, on, on targets as well.
So in this case here we have two distributions. Both of them are precise, but not accurate.
This one here in the middle, we've got on average to accurate, but they're very wide of the mark so they're actually not very precise. And if you want to have both.
Yeah, both accurate and precise.
And the other thing so that actually leads us to all kinds of decisions that we're making when we treat dates and we calibrate dates. Yeah.
So for example if we have a calibrated eight or some material which really is the year of 200, Cal dp.
Yeah.
And we, it is terrestrial material. We rated carbon dated. And if the material really is 200 years old, is truth gets
terrestrial radio carbon age of around 160 or so.
And if you can calibrate it using the correct coloration pair of the green month into 20, you get this calibrated distribution.
And yea, the year 200 actually falls well within our range so our calibrated data is a good representation of the true age because the true age falls within the range there.
If our material was Marine, so it was still 200 years old but it's actually marine and not terrestrial, then the corresponding ready cabin gear will be around 540 or so 530.
And if we don't use the correct coloration curve to marine curve, then will again calibrated, and we get, again, a coloration correction distribution where the truth is still in there, so that's fine.
But if by accident or material is Marine, but we are not aware of it I don't know why that would happen. But imagine that we use the wrong curve to calibrate our material, so it is actually marine and should have been data to the marine curve, but by
accident, we used to run curve or some wrong assumptions.
Then, are calibrated distribution will be very precise and nice.
But it's totally wrong, it does not include a 200 at all. Yeah.
Okay, so yeah we're running out of time so just wanted to go quickly to some introduction of ready, of age modeling and isn't order animation where you've got climates here, temperature that is very cold, and there's lots of ice is ice cap here and some
beautiful ASCAP and very little limitation. Yeah. and our Lake, which accumulates sediments, over time, is very lights, it doesn't really do much but then as temperatures, get higher, the ice disappears, just more vegetation and more organic material
entering the lake. Yeah. and just like keeps on accumulating.
And after a few volunteers we come here and we core dislike, and try to interprets. What happened, and then try to reconstruct meditation, over time, and eyes over time and temperature over time, and that's that's our model for that it's very important
to know when things happen so we, how to translate the depths of your core to time.
So what we want, with a step modeling when we produce a step or what we want, we want to be. Get get very precise ages yes so the more precise the better at this test.
Yeah, that would be really nice to have something no something that Decatur precision, or annual position and not a millennial precision.
And why do we want to know the ages. That's because we want to put our citing to context we want to compare it with oversights to make sense of what happened when what happened first what happened later.
We have to put some a common timescale. Otherwise, it cannot compare our different bits of information.
But what. So what we are one does not preset perhaps compare with what we actually have, because in most cases, when we have a sentiment score we only have a few dates, along the score.
yeah handful of dates perhaps might have too much money or not enough digital material.
And those days are uncertainties to might be biased. There certainly will have scatter and it will be outliers. So that's problematic.
So what we need is some way or some approach that can combine all this dating information and take care of the bias and the scatter and the outliers, to come up with a model, a time series or reconstruction of time series that is hopefully realistic and
robust to the different assumption. And also, it's good at quantifying are uncertainties. Yeah.
We're nearly out of time so I'm going to go quickly.
So if we just go and go Google for an eight step model is just one of many demos, you could choose where we've got depth on the vertical scale and age, on the horizontal scale.
And we've got a model. Yeah, curve going through some dates.
And these dates. So there was a midpoint chosen.
And the curve is going through that date and that's that's their model.
But we've already seen that radiocarbon dates, especially when they're calibrated they end up often being multimodal, and yet so it's some it's quite difficult to choose which mid points, we should use question.
And and also in this case to four days were rejected.
That was because they didn't agree with order inflammation known from the area. It was pulling information accorded said, No, there's really governance cannot be correct, you have to reject them.
And that happens, and things sometimes ready Cabernets can be wrong.
And then also, they use the model of connecting neighboring dots of accepted dates, and that it could be but how do you connect us punch you just connect the dots or chaos and draw a straight line.
And that's the types of things will be looking at. Next time, and also how to express the uncertainty. So there's no uncertainty expressed on this curve is just one curve, but we somehow have this uncertainty, we have to try to estimate that.
OK, so the next session will look at first the theory and then a tutorial of random begun for a step modeling.
And yeah, if there's any questions I'll be happy to answer them, if possible.
Yes, that's me.
Yeah, any questions at the end of our session here.
I did put in the chat.
The post session survey, so you can let us know whether there were things you were confused about if you have other questions you can ask them there and we'll address them.
At the beginning of the next session.
So, yeah, a Google Doc. Please fill it out. If you can, right now it's just a few questions, and.
Yeah.
I started
talking for two hours will do that to you.
Make sure that you guys have your. If you have your own data and your own course that you have, you know, make sure you have those ready for the subsequent sessions.
And make sure you guys are in bacon running for next time.
And we'll send out also about some information that might be on Martin's second session page about preparing your own data.
It's not we'll send something out about it but I but I bet it is so it doesn't need to be anything super complicated but if you don't have your own data, we have there, there will be example data sets that you can play with.
Yeah.
Okay, so yeah, fill out the survey, please. I'll paste it in there again just for giggles and, or you can send me an email you all have my email address from many emails from me, so.
Okay. Thanks everybody.
So good to see everyone here. Sorry least.
I was saying, thanking Martin.
Martin, and his family, and we will see you back here in a week.
Alright. Thanks so much.
See you next week.
Thank you.
No.
Thank you very much. That was great.
Cool Martin oh that was great.
Good.
Like fundamental stuff, no it's I mean I always get that when I talk for a long time.