Paris is burning. Milano is under assault. It won’t be long before London and other English cities are as well. And the primary concern of the treasonous, satanic European governments and their unelected masters is to prevent the French and Italian and British peoples from remigrating every single Third World invader.
All of this was obvious before I was even born, in 1968. All of this is inevitable. But it looks as if Reconquista Time may be approaching a little sooner than anticipated. This is still just prelude, but the arc of future history is already apparent.
Pray for the native people of Europe, St. Breivik.
Rupert Lowe asks some important questions of the Englishman
France will burn into the late morning hours. To what end? So they can harbour millions who hate everything of what is means to be French?
Was it worth it?
Will it be worth it in Britain, when similar violence inevitably erupts?
I think not.
We watch as migrants ransack Paris whilst flying the flag of various African countries.
Some may have arrived illegally, for most their presence is entirely ‘legal’. Plenty were probably born in France, and are technically ‘French’.
For the rioters, evidently, their allegiance is not to France, or to the French people.
Where does their loyalty truly lie?
Their clan.
The civilised world’s response must reflect that.
Those convicted for arson, organised violence, and attacks on innocent people must face substantial prison sentences – foreign nationals involved must be removed. Citizenships stripped from dual nationals, obviously.
Mass deportations.
Act before they do.
And in exceptional cases, I would go further.
A Restore Britain Government will develop bilateral agreements and mechanisms with third countries to accept second-generation migrant offenders convicted of domestic terrorism and other such serious crimes, who, for now, hold only British citizenship.
There will be no statelessness. It will be conducted fairly, and it will cost, but it will be done, and a message will be sent.
The French should do the same.
If you burn down a city, terrorise law-abiding citizens and torch the society around you – there will be consequences worthy of the crime.
And if your allegiance lies elsewhere, and you demonstrate that through terrorism or extreme violence, a Restore Britain Government will send you to the country you claim allegiance to – if that’s where your loyalty lies, we will aim to make that a reality.
Britain is just a few short years behind France.
We’ve had our warning – we must take it.
More warnings… yawn. The Saxon begins to hate. Try that in a small town. It’s all meaningless rhetoric and posturing. So forget responding to crime or demonstrated allegiances. All of that is avoiding the topic at hand. If a Restore Britain government doesn’t embrace mass remigration to the Third World, Britain will go the way of France. I think both the True French and the True English will triumph in the end, but not without the rivers of blood that Enoch Powell appears to have correctly predicted.
Immigration is war. Just ask the Canaanites. Or the Palestinians, for that matter.
Erika “math is hard” Matthews couldn’t quite manage to characterize my argument accurately or do the math correctly, despite reading both Probability Zero and the MITTENS 3.0 paper. But apparently, she gave it her best shot in the second video.
(16:30) Now, before we get started, I’m just going to give you Vox’s argument real quick taken directly from the horse’s mouth here and explain it to you so that you can understand why it’s problematic moving forward. We’re going to do this in baby steps. In essence, his argument is a waiting time problem, which is a specific category of creationist arguments against evolution, which essentially say there isn’t enough time for the differences we see between any two given pairs of organisms to emerge.
Instead of using any established equations or models in population genetics, Vox makes up his own equations to make this case. What he effectively ends up with is just a d= rt kind of equation or distance equals rate time time. Vox is using the human chimpanzeee divergence as his example here and he says that he is giving conventional science the most generous assumptions imaginable. Vox says that humans and chimpanzees differ by about 20 million base pairs in their 3 billion base pair long genomes. He also says that it’s been about 9 million years since the two species diverged. Given a human generation time of about 20 years, that gives us 450,000 generations in humans to accumulate all 20 million base pair differences. To account for the fact that generations overlap, which is something that population genetics can already do. Zach went over this extensively in the previous video, he introduces a new variable, which he calls the selective turnover coefficient. He calls this d, and he assigns it a value of one.
And finally, Vox provides us our rate by saying he is using the fastest known rate of mutational fixation, which he claims comes from the 2009 LTE Orinsky long-term evolution experiment observed in populations of E.coli. Since he says this is the fastest observed rate, he describes this as the speed limit for evolution or the ceiling, the fastest it can possibly occur. And the number he provides is 1,600. 1,600 is the number of generations it takes for one mutation to fix.
That word fix is important because the fixation rate is different from the mutation rate. The mutation rate is the rate at which mutations occur. And the fixation rate is the rate at which they come to define a population. Let’s use fur color and foxes as an example here. The fixation rate would be the rate at which a newly emerged coat color, let’s say red, comes to characterize the entire population of foxes. You can see then that the mutation rate is going to be quite different from the fixation rate with the latter being determined by a myriad of different factors within the environment. And as all sources, including Wikipedia will tell you, fixation occurs through natural selection, genetic drift, or both. So, as Vox will tell you, his math here is pretty simple. 9 million years of divergence time 1, the selective coefficient, divided by the generation time, that’s 20 years, times the generation it takes for one mutation to fix, or 1600. And that equals 281ish. And 281 is way less than 20 million, right?
So, that’s it. Evolution killed by a fraction that no one ever thought of. Well, I mean, as long as all the variables are correct and as long as the assumptions which go into this equation are met and or justified, if any of those variables are wrong, then the entire equation is bunk. And if the assumptions that go into those variables are wrong, the equation is also bunk. So, it’s got to be all right. Right? Wrong.
That’s her critique of MITTENS. I’ll now go over what she said and point out eight of her obvious errors.
ERROR 1:MITTENS is not the waiting time problem. The accounting issue I have identified is not the waiting time problem. She doesn’t understand that these are two entirely different arguments that are focused on opposite sides of the fixation process. The waiting time prolem refers to the significant amount of time required for beneficial genetic mutations to occur in a population, particularly when multiple coordinated mutations are needed for adaptation. The MITTENS argument isn’t concerned with mutation rates at all, it is an empirical audit of observable fixations. It doesn’t model theoretical waiting times; it measures observed throughput. Categorizing it as a “creationist argument” is not only false, but dishonest. The question of whether there enough time to account for the observed genetic divergences is a quantitative question that absolutely must have a specific quantitative answer.
ERROR 2: I did not make up the equation for determining an average rate” — Matthews exhibits the same inability to understand the concept of an average rate that Dr. Hanson demonstrated in the first video. I didn’t make up a new equation. I simply observed that given the amount of time, the time it takes for an event, and the number of events involved, the average rate required was improbably high to the point of effective impossibility.
ERROR 3: 410 million is not 20 million. Matthews is off by more than an order of magnitude. We know, by her own admission, that she has read MITTENS 3.0. The total divergence reported there is 410 million, with 205 million assigned to the human side. This number is slightly exaggerated because the Yoo et al 82-page supplementary spreadsheet reports three different numbers on three different pages with different denominators; a direct accounting directly from the raw data results in a total genomic human-chimp divergence of 398,673,272 base pars. She’s trying to criticize my math when she’s literally off by 95 percent on the total that must be accounted for.
ERROR 4: 9 million is not 6.3 million. She’s trying to fudge the numbers while claiming to present my argument faithfully.
ERROR 5: 20 is not 25. MITTENS uses the standard 25 years per human generation, consistent with paleodemographic reconstructions (Langergraber et al. 2012). This is another error that just happens to make the argument appear a little weaker by a) providing more generations and b) citing a different number of generations than I reported.
ERROR 6: 450,000 is not 201,600. This falls out from her false inputs (9M / 20 = 450,000). The actual number of available generations prior to the correction for generational overlap is 252,000 generations (6.3M / 25). She’s arbitrarily providing the evolutionary process more than twice as many generations as are actually contained in the argument presented, which is 201,600 effective human generations.
ERROR 7:The post-CHLCA human value of d is 0.80 not 1.0. She completely misunderstands what the Selective Turnover Coefficient is and why it is necessary. Setting d = 1 is the Wright-Fisher idealization. It’s the value for the E. coli of the LTEE, it cannot be the value for humans with overlapping generations. This is the result of Dr. Hancock’s inability to distinguish between Wright-Fisher’s and Hall’s use of overlapping generations to calculate drift and d‘s use of them to correct for the available number of effective generations over time. If humans were bacteria, there would be time for 252,000 generations. Because we are not, there was time for 201,600.
ERROR 8: 71,000x is not 1,139,000. Her own arithmetic from her wrong inputs: (9,000,000 × 1) / (20 × 1,600) = 281.25. The shortfall she calculates against 20 million (~71,000×) is actually much smaller than the MITTENS shortfall (~1,423,611×) because every one of her input errors runs in evolution’s favor. The actual calculation yields 144 fixations against 205 million required fixations. But, she’s right to observe that 281 is a small fraction of 20 million. It’s an even smaller fraction of 205 million.
ERROR 8: “If any of those variables are wrong, then the entire equation is bunk” This fails to grasp the scale of the argument’s architecture. The million-fold shortfall means it is necessary to change the inputs by orders of magnitude in order to close the gap. The margins aren’t close. There are no small corrections to a variable that will render the proof false. Only very, very large and significant structural changes are capable of showing it to be false.
Now, I’ve actively looked deep into those potential structural changes. I’ve run various analyses of the raw data of the LTEE. I’ve looked into alternative evolutionary mechanisms, and thanks to correcting the application of Kimura’s time equation, actually managed to find and empirically confirm that I call “relictation” which is neither drift nor natural selection, and can, in certain circumstances, reduce the time necessary for neutral drift to fixate by cutting it in half. It’s nowhere nearly enough to render MITTENS false of course, but it is an iterative improvement in the analytical model.
However, the complete analysis of the 25,302,032,866 base pairs for the seven species of Human, Chimp, Bonobo, Gorilla, Borneo Orangutan, Sumatran Orangutan, and Siamang does open a brand new line of attack against MITTENS that will either a) potentially permit the refutation of MITTENS in one or more divergences or b) weld shut the locked door that has already been closed against evolution by natural selection, neutral drift, relictation, and every other proposed mechanism.
But, as should be abundantly clear by now, if MITTENS is ever going to be refuted and evolution is ever going to be shown to be mathematically and empirically possible, it’s not going to be by Erika and her innumerate Evo-Avengers. We’ll see if they eventually managed to do any better than she did in the initial part of the video, but their collective case is obviously not off to a promising start.
So Paris is burning again. One of the small satisfactions of watching the real-time collapse of Western civilization is seeing how those who welcomed it are among the first to suffer the consequences.
A liberal French women explains to these African Islamic invaders robbing her family’s store that she’s on their side & voted for them to come here & voted to defund the police. They laughed and mocked her and stole all her merchandise.
It’s not the racist and nationalist young women who are getting raped and murdered, it’s the “welcome refugees” morons. It’s not the bigots in the countryside whose businesses are being pillaged, it’s the big city liberals who imagine that they are on the same side as the invaders.
I’m not saying that what is happening to them is good. But what is happening to them is, without question, well-deserved. And better it happen to them than to those who have opposed it all along. So don’t pity them. Don’t defend them.
Eventually, inevitably, the reconquistas will begin. And there will be no one left to stand in the way. The invaders will ensure that, one way or another.
There is a massive difference between the innumerate Evo-Avengers committing one mathematical error after another in trying, and failing, to criticize Probability Zero and the analyses of the possibility of evolution by various means proposed it contains, and a critical review by an actual mathematician, in this case, Dr. Ola Hössjer, a Professor of Mathematical Statistics at Stockholm University
1. Introduction On September 16, 2026, I wrote a Substack guest-post review of the first edition of Vox Day’s book Probability Zero. In this book, Day presents an argument for the Mathematical Impossibility of The Theory of Evolution by Natural Selection (MITTENS). Since Day updated his argument in the second edition of Probability Zero, I am here writing a more extensive follow-up review based on the second edition of Probability Zero and some other written material of Vox Day.
Recall that Vox Day in Probability Zero bases his arguments for the mathematical impossibility of blind evolution on genetic comparisons between humans and chimpanzees. Day claims that the time available since the supposed Chimpanzee-Human Last Common Ancestor (CHLCA) population lived is not enough to explain the genetic differences between humans and chimpanzees today. These arguments are on one hand based on population genetics (the mathematical framework for evolutionary theory) and on the other hand the outcomes of the 60,000 generations Long-Term Evolutionary Experiment (LTEE) for E. coli bacteria, conducted by Richard Lenski and his collaborators.
In the second edition of Probability Zero, Vox Day has updated some estimates of the genetic differences between humans and chimps, based on a recent Nature study from 2025, where the complete genomes of six great ape species, including chimpanzees, were sequenced. These new data made it possible to compare the human and chimpanzee genomes in more detail. Whereas previous comparisons between human and chimp DNA, based on incompletely sequenced data, revealed a 1.2% difference between the two genomes, in this more recent analysis from 2025 a much larger 14% difference was found. Whereas most of this 14% difference refers to insertions, deletions and larger structural variations, the single nucleotide variant (SNV) differences, along those parts of the human and chimpanzee genomes that can be aligned, still account for about 1.2%. Not surprisingly, these new data with a 14% DNA difference make it even harder to explain the genetic differences between humans and chimps by means of blind evolution.
In this second blog post, I will discuss some of the mathematical challenges that the theory of evolution faces. My arguments are not a full review of Day’s work. They are based on that material from the second edition of Probability Zero, the accompanying book The Frozen Gene and several other articles by Vox Day that are relevant for assessing whether blind evolution can explain common ancestry between human and chimps. My first review of Probability Zero was largely positive, and this impression remains after having read more of Day’s work. I agree with many of his conclusions, and the points where we differ are not important for the main arguments. Compared to my first blog post, in this second article I will focus more on mathematical and population genetic details. To keep the text accessible to a wider audience, most of the mathematics appear in footnotes.
2. Basic parameters I will follow the assumptions in the second edition of Probability Zero, that the genomes of humans and chimps are about 𝐿 = 3 · 109 (3 billion) nucleotides long, that the supposed CHLCA population lived 𝑇 = 6.3 · 106 (6.3 million) years ago, with a generation time of 𝑇𝑔𝑒𝑛 = 25 years after the split along the human lineage, corresponding to 𝐺 = 𝑇𝑔𝑒𝑛 = 252, 000 generations. These generations are partially overlapping, and Day estimates that the fraction of individuals that are replaced in each generation is 𝑑 = 0.8, the so-called turnover rate. This implies that the effective generation length 𝑇𝑔𝑒𝑛 𝑑 = 25 0.8 = 31 of years corresponds to an expected lifetime, and that 𝐺𝑒𝑓𝑓 = 252, 000 · 0.8 = 201,600 effective generations elapsed from the CHLCA population until today. The number of SNV differences between humans and chimps is roughly 1.2% of 3 billion, or 35 million. If half of these SNVs (17.5 million) first appeared as point mutations (single nucleotide mutations) in one individual along the human lineage and then spread to the whole population (so called fixations of these mutations), the required number of fixations along the human lineage for evolutionary theory to explain is 𝐹𝑟𝑒𝑞 = 17.5 million. I will assume that the probability of a point mutation for humans is 𝜇 = 1.2 · 10−8 per nucleotide and generation (the so-called pointwise mutation rate).
In this second blog post, I will mainly concentrate on the 1.2% (or 35 million) SNVs differences between humans and chimps, or the 17.5 million required fixations along the human ancestral lineage. Then at the end I will also comment on the remaining 12.8% genetic differences between the two species. This will reinforce Vox Day’s conclusion that no known mechanism of blind evolution can explain common ancestry between humans and chimpanzees.
Prof. Hössjer has put the whole paper on Zenodo, where you can read the whole thing if you are mathematically inclined. But, I can assure both you and the skeptical biologists and population geneticists, he is far from the only mathematician to have critically reviewed my work. In addition to providing early version of both PZ and TFG to ten mathematicians and physicists prior to the publication of the first editions, none of whom were Prof. Hössjer, with whom I was hitherto unfamiliar, I’ve recently received a 36-page paper from one of those pre-release reviewers who is presently checking some of my post-PZ math on relictation and the mathematical boundaries of Kimura’s time equation.
I hasten to point out that none of this means there aren’t legitimate mathematical challenges to my assertion of evolution’s mathematical impossibility. There are, they will be presented, they must be analyzed in full, and they will be. Like any good iterative designer, I’m methodically working through those potential challenges at increasingly higher resolution and greater levels of detail as the various equations are better understood and refined. Of course, it only falls to me to do so because the scientists in the relevant fields have proven to be too innumerate to grasp the obvious legitimacy of even the earliest, crudest forms of the proof.
The demonstration of mathematical impossibility may yet be wrong. It is not, and it can never be, irrelevant.
Is it still theoretically possible that I’m wrong and that the post-modern polythesis will somehow survive my mathematical analysis? In theory, yes. I can currently construct one analogous model based on real empirical evidence that may or may not be directly applicable to the chimp-human divergence that would, in theory, produce enough base pairs to satisfy the observed shortfall. But only one, while all of the other constructed models point the other direction, and we now have an entire matrix of comprehensive divergences that the polythesis model has to somehow satisfy perfectly to even hope to remain in play.
It’s a little ironic that all of this work will be performed and published outside the relevant scientific fields, but that’s what happens when the evolutionary biologists are too lazy and innumerate to join in all the big boy games.
One of the things that the evolutionists like to use to disqualify Probability Zero is describing it as “AI-written”. Which is, of course, partly true. But that should frighten them, not comfort them. Because it is evidence of the strength of the book’s legitimacy, not the opposite.
Remember when they said to “Trust the science?”
Artificial intelligence finds that a quarter million cancer research papers have been faked
A 2026 study trained an AI on 2,202 cancer papers that had been identified as fake. It then applied this model to look into 2.65 million original cancer research papers published between 1999 and 2024. It flagged 261,245 papers as fake
Over 178,000 flagged papers had Chinese institutional affiliations
Roughly 36% of Chinese cancer research output in the dataset
Iran was next at around 20%
This is a major problem because this can mean the medical community amplifies completely fake research and findings
“Every fabricated study sitting in a real journal is competing for attention with studies actually pointing towards a legit cure, which means the more fake noise there is, the longer it takes for the real breakthroughs to surface. And somewhere in that pile, maybe a real cure is buried under a fake AI-generated one.”
No doctors have gone to jail for this (I checked)
This also demonstrates how the fact that MITTENS isn’t peer-reviewed means nothing at all. And it’s interesting that they describe it that way, because, as it happens, it has been reviewed by a number of people, including mathematicians and physicists, as will be demonstrated later today.
Philadelphia Eagles QB Jalen Hurts will miss Sunday’s game due to a blood clot. And according to one former NFL doctor, he may miss more:
David Chao served as the Chargers team doctor for 17 years while they were still in San Diego, so when he studied the statement the Philadelphia Eagles released about Jalen Hurts and his blood clot, the doctor focused on one key word that will determine whether the quarterback misses a little time or a long time.
The word that glowed in neon for Dr. Chao was “movement.”
The Eagles’ statement announcing Hurts is out for Sunday’s game against the Carolina Panthers said that a follow-up ultrasound on Wednesday revealed “movement in the small blood clot” the team had discovered four days earlier.
“What do they mean by movement?” Dr. Chao told OutKick. “Do they mean movement as in it’s propagating and it’s now a bigger superficial blood clot? Or is it movement meaning it’s deeper and it’s a deep vein clot?
“If he has a deep vein clot, he’s out for multiple weeks, at least.”
Somewhere in Pennsylvania, Aaron Rodgers is having the last laugh.
UPDATE: The Philadelphia Eagles have placed quarterback Jalen Hurts on injured reserve after he was diagnosed with a superficial blood clot. Hurts will now miss a minimum of four games.
This will be the final response to the first Evo-Avengers video, and it’s chiefly of interest for the way it demonstrates once more that biologists simply don’t understand the concept of averages. We saw that with JF Gariepy. And now we see it demonstrated here by Dr. Hancock being mystified by the concept of an average rate of fixation.
They really are innumerate.
(1:29:30) We’re going to follow his math and I’m going to give you sort of the logic which you just saw, but we’ll do it again so that you can, you know, the more times you see it, the better it’ll cement in your in your brain. Okay, he then says fmax is equal to t over g which is the divergence time divided by generation. So we’re basically converting it to generations over k and k we’re going to call the fixation rate.
What he is what he is saying is the maximum fixation rate that has ever been observed. Right? That’s that 1400 generations. So he has this other term D, but I don’t we don’t know what it means. We don’t we don’t know how to how to correct and it’s just one in his equation anyway, so we’ll just ignore it. Um, so he then claims that this ratio is 252,000 generations over 1,400 generations to fix a mutation. So we’ve got 250,000 generations and each mutation takes 1,400 generations to fix. That’s that’s basically what he’s saying. And of course that equals 180 expected fixed differences. You follow that so far? Roger. Okay.
So that all might seem okay to you if you if like maybe you’re not like particularly mathematically minded or maybe you don’t know a ton about population genetics, but let’s break down what this actually means. Um and I think the best way to show it is actually graphically. So we’re going to we’re going to test my artistic skills here. Um so this is the frequency which we’ll call f of p the frequency of p and then here is time on the x- axis. Okay. And we have out to exactly 252,000 generations. That’s how much time that we have to to start fixing things, right? So if we start at time zero, we get a mutation. Now he claims it takes 1400 generations for that mutation to fix.
So we we go up we eventually reach fixation and then this interval here is 1,400 generations. That’s what he’s claiming, right? Yeah. So we’ve now fixed one mutation. Then we can get another mutation here and then it has to go to fixation and then this interval as well is 1,400 generations. And you just keep doing that over and over and over until you get to 250,000, right? And how many little intervals does it take to get to 250,000? It takes 180 intervals to get there. So hopefully you can see a a very glaring problem with this math. One is that it assumes that each mutation has to both arise and go to fixation before the next mutation can occur. Right?
Because otherwise if you can allow multiple mutations to occur at the same time that means multiple muta multiple mutations can fix at the same time. But that is not allowed in this math. There must be these discrete intervals. And we know that because it takes 180 intervals to get to 252,000. Right? That’s literally what that math is showing. Does that make sense? Yeah. He’s defining it in with the equation he’s choosing to use. Yeah. in effect he’s he’s the the way that this equation is set up assumes that these these mutations must arise and fix sequentially. Um that’s the the basic assumption and to me that makes this whole thing preposterous, right?
The calculation T/k = 252,000/1,400 = 180 is a rate calculation. It says: given an observed fixation rate of one per 1,400 generations, how many fixations fit in 252,000 generations? The answer is 180. That’s throughput, or output per unit time.
However, Hancock somehow interprets it as a latency calculation and assumes that each fixation must complete before the next one even starts, like cars going through a single-lane tunnel one at a time. Then he triumphantly announces that mutations can fix in parallel, as if that was ever stated or successfully refutes anything.
The problem here is that observed rate already includes whatever parallelism actually occurs. Specifically, it includes, on average 5.1 neutral hitchhikers with each selective adaptation. Because that’s the primary form that “parallel fixation” takes, not multiple selection events completing at the same time, but five neutral hitchhikers being swept to fixation with the selected mutation. This boost to the throughput is already reflected in the observed fixation rate. The rate is the rate. It doesn’t matter whether it comes from one fixation at a time taking 1,400 generations or seven fixations running in parallel each taking 9,800 generations. The output is the same: 144 in 201,600 effective generations.
And here’s the irony: if he wants to argue for massive parallel fixation to increase throughput beyond the observed 144, he has to deal with Hill-Robertson interference, the fact that simultaneously segregating beneficial mutations compete with each other and slow each other’s rate of fixation down.
So what Hancock did was to draw a picture of sequential fixation on a whiteboard, falsely asserted it was my assumption, and then refuted his own drawing.
Now, what Dr. Hancock didn’t know, because he didn’t bother to read my data paper on the LTEE, is that this is neither what the experiment data shows nor does it represent my argument correctly. First, because he didn’t understand why d is necessary to correct for overlapping generations, he uses the wrong number of generations: 252,000 instead of 201,600. Second, we’re in the process of considerably increasing the accuracy of the denominator from fixation to base-pair, which is very important, but doesn’t matter for now. Third, he also didn’t know that I have independently run the LTEE data for all 723,000 generations covering all 12 populations at a much higher resolution than is published in any of the papers; 500 generations rather than 10,000 generations.
So, instead of guessing and theorizing about what sort of fixations appeared and when they appeared, I know exactly what happened. And here is what the data actually looks like for the one of the fastest of the five remaining healthy populations, Ara+4, over its 60,000 generations.
That’s not theory. That’s what actually happened. The red are the fixation events. There are 16 of them. The yellow are the 64 neutral hitchhikers that also fixed during the 11 sweep events. So what this shows is neither serial fixation nor what people usually imagine parallel fixation would look like; there was never one single overlapping fixation event in any of the 5 normal populations or the 7 hypermutating ones. Interestingly enough, the hypermutational populations actually had fewer fixation events, although the sweeps were considerably larger, as can be seen in the most active hypermutational population, Ara+6, with 9 vs 16 fixation events and 2581 hitchhikers vs 64.
It’s easy to see when Ara+6 broke down; the massive sweep that started at generation 6k and extended over 14k generations is the result of the broken DNA repair mutation; essentially, the genome developed cancer then. Notice, however, that both the normal and the cancerous population had gaps that ranged from a minimum of 1,500 generations in between sweeps up to a maximum of 7,000 generations. So the idea that there is any parallelism that can somehow magically speed up the process to a rate that could possibly account for the size of the observed divergences between all of the 7 ape species is an absolute non-starter.
In fact, the LTEE data reveals something the butterfly collectors exercising their imaginations rarely contemplate: the distinction between how often fixations complete and how long each one actually takes. Which means we can measure the parallelism of each population as their mutations rise toward fixation at the same time. Across the five non-mutator populations, an average of 18 mutations that eventually fixed were simultaneously rising through the population at any given timepoint. The all-transit depth was 43, meaning that 25 other mutations entered the 5–95% frequency zone, competed for survival, and eventually faded before they could fixate.
And the variation across populations is significant: Ara+2’s completing depth of 5.24 versus Ara−5’s 33.35 represents a sixfold difference in the degree of parallelism between populations of the same organism in the same environment. But not even the highest degree of parallelism observed is anywhere close enough to the degree envisioned to permit significantly reducing the number of generations per fixation.
The Ukrainian corruption is beginning to be permitted to reach the media:
Weapons supplied to Ukraine from US military stocks have been found in the possession of Sudan’s paramilitary Rapid Support Forces (RSF), a senior Russian diplomat has said, citing markings linking them to deliveries to Kiev.
Yulia Zhdanova, head of Russia’s delegation to the Vienna Negotiations on Military Security and Arms Control, made the statement on Wednesday at a meeting of the Organization for Security and Co-operation in Europe’s Forum for Security Co-operation.
Sudanese troops recovered MK19 automatic grenade launchers, M72 anti-tank weapons, and Swedish-made AT4 launchers during an operation near Bara, she said, according to TASS. “A preliminary examination of the markings revealed that these weapons were donated to the Ukrainian Armed Forces in December 2024 from US military stock,” Zhdanova said.
It won’t surprise me in the slightest if we eventually learn that the Houthis are conquering Saudi Arabia with weapons purchased through Ukraine.
