Tag: evolution

Chalub and the Kimura Cancellation

In 2022 a Portuguese mathematician named Fabio Chalub published a paper solving the neutral Kimura equation called Solution of the Neutral Kimura equation with two integral constraints. It is not a critique of neutral theory. Chalub gives no sign that he is even aware that anyone is contesting neutral theory. He is a specialist in degenerate partial differential equations doing what specialists do, which is testing the assumption that a well-known equation actually has the solution everyone has assumed it has.

Chalub opens by pointing out something that seventy years of science textbooks have failed to notice. The classical solution to the neutral Kimura equation — the one Kimura wrote down in 1955, the one reproduced in Crow and Kimura and in every graduate course since — decays to zero. All of the probability drains out of the interior of the interval and nothing is left. As a description of a population, it is nonsense: the alleles have to go somewhere, which is either fixation or extinction. The standard solution does not track them. It simply stops accounting for them. To fix this, Chalub adds two point masses at the boundaries, one representing the fraction of populations in which the allele has fixed and one representing the fraction in which it has been lost. Fine. Now comes the interesting part.

Nothing in the Kimura equation determines how the escaping probability splits between those two boundaries. The equation degenerates at both endpoints — the diffusion term goes to zero there — and there is no condition inside the mathematics that says how much goes to fixation and how much goes to extinction. The equation upon which the entire molecular clock is built does not provide the one number that it needs. So Chalub supplies the number from outside, in the form of two constraints he imposes by hand before solving anything. The first is that total probability is conserved. The second is that the average allele frequency never changes, which is not a fact about diffusion but an inherited assumption from the Wright-Fisher model, the very assumption that has been successfully disputed by me and three scientists before me. Impose that assumption and the fixation probability comes out equal to the starting frequency. Of course it does. Chalub writes in the constraint before he solves anything, recovers it as a result two pages later, and he says plainly that the constraints are inherited from the discrete process.

He has no reason to be aware of what he has demonstrated. The diffusion machinery is invoked throughout the literature as independent confirmation that a neutral allele fixes with probability equal to its frequency. Chalub shows the machinery cannot confirm any such thing, because it was given the answer in advance. That is the circular reasoning. Chalub shows that the 1/(2N) is an assumption imported from the Wright-Fisher model. It is not a result produced by the mathematics. It was never tested against, or derived from, an actual population.

This changes the shape of the Neutral theory critique even further in my favor, not that I needed any more ammunition given the N/Ne switcheroo as well as Frankham’s empirical evidence that conclusively demonstrates the inapplicability of Kimura to sexually reproducing populations. But until now, the defense of Kimura’s cancellation still had one retreat available: the critic does not understand the diffusion approximation, the result is a theorem of the continuum limit, the mathematics is above his pay grade.

That retreat is now closed, and it was eliminated by a mathematician who was not even paying any attention to its manifold implications for evolution, neutral theory, or the molecular clock. The initial-frequency theorem is not a theorem of the diffusion. The diffusion cannot produce it. The diffusion has to be provided as an assumed condition that is supplied in order to solve the equation. Which means that when we say Kimura’s cancellation rests on an assumption about how populations reproduce rather than on the math, we are no longer making an assertion that requires the reader to side with us against the scientific consensus on the basis of the evidence and the logic. We are simply repeating an observation that has been independently established in the mathematical literature and can be confirmed by anyone with the math to do so.

Kimura’s assumption about populations were always the whole of his argument. Chalub proved this, published it, and no one in population genetics appears to have either read it or realized its implications.

And if you don’t grasp what this means, here is the short version: every molecular single clock has been eliminated. All of the scientific estimates about deep time are gone. Even my own attempts at recalibration are hopeless, irrelevant, and wrong. It’s not just the CHLCA estimate. It’s also the Cambrian “molecular dates predate the fossils” literature, which was used for two decades to argue the fossil record was systematically incomplete rather than that the dates were all systematically wrong. This includes the mammalian radiation and whether it preceded or followed the K-Pg. The out-of-Africa chronology. Every single eukaryotic node in TimeTree. And the dating of angiosperm origins that produced Darwin’s abominable mystery in its modern form.

All of these time frames were calculated with a rate constant that has no derivation and no non-circular calibration. And all of it is now worthless and mathematically invalid nonsense.

For those who still have no idea what I’m talking about, let me make it even more straightforward for you. And rest assured there isn’t an evolutionary biologist on the planet who can even begin to successfully argue against any of this.

Dear Biologists,

Evolution is mathematically impossible. Your historical time estimates are not only wrong, they cannot even be calculated on the basis of the information you have. And your most important equations from your best guys are mathematically nonsensical.

Love,

The Mathematicians

DISCUSS ON SG

Another Failed Critique

The Kurgan discovers that AI critiques of Probability Zero cannot be relied upon.

In going over the summaries I did of your work and getting AI to check I have fairly represented what you say, I got the following, which I believe to be a narrative/adjacent/hallucination type of error AI is very prone to, as I have now become very familiar with. But I have not had time to review your latest updates, so I thought it best to get it direct from you.

The issue I am having is the text the AI spat out below when I basically said its objections didn’t make sense. It tried to say that not ALL models assume 100% replacement per generation as if that somehow invalidate your conclusions, (it had to concede almost all do use 100% replacement) So, I told it : “But in any case, even if a model does NOT assume 100% replacement that just makes Vox’s point even more valid.” This was its reply:

Does an incomplete-turnover model make Vox’s point more valid? No — and this is the part I have to be straight with you about, because it’s a vulnerability sitting in plain sight.

There are two claims tangled together:

1. Turnover is incomplete — generations overlap, not everyone is replaced each generation.
2. Incomplete turnover slows evolution, by something like a factor of d.

Claim 1 is trivially true and nobody disputes it — and yes, the Moran model “agrees.” But the entire force of the Selective Turnover Coefficient rests on Claim 2, and Claim 2 is precisely what the overlapping-generation models refute. Work evolution out in the Moran model — the rigorous overlapping-generations treatment — and you do not get a 1/dslowdown. You get essentially Wright–Fisher dynamics, and for drift, if anything, slightly faster. The unambiguous version: the substitution rate of neutral mutations equals the mutation rate whether or not generations overlap (Kimura’s result is model-independent), and a beneficial allele still fixes with probability ≈ 2s (Haldane). The engine runs at the same speed regardless of what fraction turns over per nominal “generation.”

What the d ≈ 0.45 factor actually is, is a units problem, not a biological brake. If only 45% of the population is replaced in what you’re calling a “generation,” then your “generation” is just shorter than the real mean generation time — you’ve relabelled the clock, not slowed it. The real biology (births, deaths, selection per year) is untouched, and mainstream models already handle it by using the actual generation time. There’s no hidden 1/d penalty waiting to be recovered.

So the strategic position: the moment you correctly narrow the claim to “Wright–Fisher,” you’ve conceded overlapping-generation models exist — and a population geneticist’s very next sentence is “right, and the Moran model does overlapping generations and produces no turnover slowdown, so where does the Bio-Cycle Fixation Model’s 1/d actually come from?” The burden then falls on Vox’s model to explain why it diverges from the standard overlapping-generation result, and I don’t think it can.

Error 1: “Kimura’s result is model-independent.”

This is false. The book addresses this explicitly. The derivation of k = μ goes:

k = 2N · μ · 1/(2N) = μ

The cancellation depends entirely on the initial-frequency theorem: the fixation probability of a new neutral allele equals its initial frequency, which is 1/(2N). And that result depends on exchangeability — every gene copy in the population must have the same probability of being the ancestor of the entire future population. In a Wright-Fisher model with discrete generations, exchangeability holds by construction. In a real sexual population with overlapping generations, it fails, because gene copies carried by a 20-year-old with forty years of reproduction ahead of her are not equivalent to gene copies carried by a 55-year-old with two years left. Their probabilities of fixation differ because their expected reproductive contributions differ. Once exchangeability fails, the fixation probability is no longer 1/(2N), the cancellation doesn’t go through, and k ≠ μ.

The critic asserts model-independence without engaging the assumption on which the derivation depends. That’s not a refutation. It’s a restatement of the claim being challenged.

Error 2: “The Moran model does overlapping generations and produces no turnover slowdown.”

The Moran model replaces one individual per time step — one birth, one death, chosen uniformly at random. It is “overlapping” in the trivial sense that not everyone dies at once. But it preserves exchangeability by construction: every individual is equally likely to be chosen for reproduction and equally likely to be chosen for death. There is no age structure, no differential reproductive value, no biological reality in which a grandmother and a teenager have different expected future contributions to the gene pool.

The whole point of the Selective Turnover Coefficient is that real overlapping generations are not Moran-style overlapping generations. In a real human population, individuals who were already adults in generation N are still reproducing in generation N+1, and they carry their existing allele frequencies forward, diluting the effect of selection on the new cohort. The Moran model abstracts this away by making every individual interchangeable at every time step. Citing it as evidence against d is citing a model that assumes exchangeability to refute an argument that exchangeability fails. That’s circular.

Furthermore, the Moran model isn’t the one that is relied upon by any biologists anyhow. The Moran model is a less-effective attempt to correct for the very Kimura-Wright-Fisher model that is the standard in population genetics.

Error 3: “d is just a units problem — you’ve relabeled the clock, not slowed it.”

This is wrong, and the book explains exactly why.

If you redefine the “generation” to be longer (so that 100% turnover occurs per redefined generation), you get fewer generations over the divergence interval. The math doesn’t change because d enters the calculation twice and in the same direction: it reduces both the effective selection per generation (Δp ≈ d · s · p(1-p)) and the number of effective generations (G_eff = G · d). You can’t escape this by rescaling one without rescaling the other. The total selective work done over the divergence period is d² times what the discrete model predicts, not d times, and no unit conversion eliminates a squared factor.

More importantly, the “units problem” claim is empirically falsified. If d were merely a relabeling, then the standard Kimura model and the Bio-Cycle model would produce the same predictions for allele frequency trajectories. They don’t. The book tests both models against three independent ancient DNA time series — LCT, SLC45A2, and TYR — using published selection coefficients. Kimura systematically overpredicts, driving alleles to near-fixation when observed frequencies are substantially lower. The Bio-Cycle model with d ≈ 0.45 for the Neolithic reduces prediction error by an average of 69% across all three loci. Three independent loci, different selection pressures, different time periods, different geographic regions, all converging on the same correction factor. A “units problem” doesn’t produce systematic overprediction in one model and accurate prediction in the corrected model. A real biological constraint does.

The AI critic’s objection follows a familiar pattern. It defends k = μ by citing models (Wright-Fisher, Moran) that assume the very thing being contested (exchangeability), calls the correction a relabeling rather than a physical constraint, and never engages with the empirical validation that distinguishes the two models. It is, in short, exactly the kind of narrative objection that sounds rigorous until you check whether it actually addresses the math — at which point you discover it doesn’t.

DISCUSS ON SG

Taleb, Anti-Fragility, and Evolution

A reader writes in about anti-fragility and evolution:

I’m reading “Anti-Fragile” and, like Taleb’s other books, appreciate his insight. I do find it interesting how much he argues for anti-fragility using evolutionary and enlightenment examples. I agree with Taleb’s main point on anti-fragility. However, having read “Probability Zero” and your recent posts on Kant, I’m thinking you’ve refuted all his arguments to illustrate anti-fragility via evolution and enlightenment thinking. Taleb spends quite a bit of time writing about how evolution “directs” certain outcomes or how enlightenment thinking supports his humanism position.

Do you know if Taleb has read your recent works? His math background would hopefully allow him to fully engage in the math you’ve revealed.

My limited understanding is you’ve shown evolution can’t direct anything or even happen. The changes we see are either random or directed by some intelligence. Secondly, by refuting Kant and enlightenment thinking, would that impact Taleb’s thinking on how he approaches so many things being “unknowable”?

Taleb was familiar with SJWs Always Lie. I doubt he is familiar with any of my newer work. While we had some contact on Twitter beating up on Mary Beard and her ahistorical nonsense together, I have had no contact with him since getting banned from there in 2017 or whenever it was.

This is where I think it’s always vital to distinguish between the What and the Why. Anti-fragility is a sound concept and an important strategy that does not rely in any way, shape, or form on Taleb’s attempt to explain it in terms of evolution by natural selection or Enlightenment illogic.

One minor correction: the changes we see are not random. Kimura-style neutral drift has also been disproven in Probability Zero and The Frozen Gene, although the disproof was totally unnecessary because anyone who actually understood the math would never have pretended it could even begin to fill in the gaps produced by the insufficient rate of evolution natural selection; the drift equation is 40x slower to fixate than the already-too-slow rates examined in my books.

DISCUSS ON SG

The Irony of the 8s

People who believe the earth was created 6000 years ago, when it’s actually 4.5 billion years old, should also believe the width of North America is 8 yards. That is the scale of the error.
—Richard Dawkins

And 8 yards has to be wrong, because an evolutionary biologist like Richard Dawkins believes that the width of North America is 8 and 1/4 inches. That is the scale of the error committed by someone who believes in the evolution of Man and thinks that there was time for the evolution of 205,000,000 base pairs in the time that was sufficient for, ironically, 8.

It’s more than a little amusing to see how evolutionists are observably worse at science than young-earth creationists.

Read the 2nd edition of Probability Zero, the number one bestseller in Biology, Evolution, and Genetic Science if you want to see how comprehensively and conclusively that statement is backed up. The hardcover and paperback editions will be available soon.

DISCUSS ON SG

An Existential Crisis

It’s a little hard to take seriously the warnings of those who proclaim an “existential crisis” due to declining fertility rates when they won’t even address the primary cause of those declining rates and are not aware of the primary cause in declining fertility. Even when the crisis is real.

The U.S. is facing a worsening fertility crisis, according to analysts.

While the nation’s fertility rate has been declining for decades, it dropped to a new record low in 2025. Experts told the Daily Caller News Foundation that deregulation, improving fertility care and bringing down costs related to raising children could help boost the declining birth rate.

The U.S. general fertility rate was 53.1 births per 1,000 women aged 15 to 44 in 2025, down from 53.8 in 2024, according to National Center for Health Statistics data published in April.

“While there are many factors contributing to the declining birth rate, three reasons stand out to me: First, there is the influence of smart phones and social media,” Heritage Foundation Senior Policy Analyst Emma Water told the DCNF. “Since the introduction of the iPhone, [every] country has seen a marked decline in births that doesn’t look like it is reversing any time soon, including the U.S. … we are seeing more men and women replace meaningful time with others with scrolling, screen addictions, or a sense that there is too much to be done.”

“Second, we cannot discount the role of abortion, birth control, and reproductive technologies,” Waters said. “While we can have a meaningful conversation about the morality of each separately, the statistics don’t lie: The last year that the birth rate was above replacement was 1972, and since the Supreme Court decision in Roe v. Wade erroneously created a constitutional right to abortion in 1973, the birth rate has never recovered.”

Waters added that a drop in U.S. marriage rates is one of the “primary drivers of declining birth rates.”

The U.S. marriage rate dropped to a 140-year low in 2019 and has yet to fully bounce back, The New York Times reported. Less than half of American households were married couples in 2025, marking a significant decrease from 50 years earlier, according to U.S. Census Bureau estimates.

Prioritizing infertility treatment and early diagnosis could help boost the U.S. fertility rate, according to Waters.

First, prioritizing infertility treatment will only make matters worse. Average female fertility has been dropping steadily since 1900 due to the frozen gene and the inability of natural selection to continue keeping the human genome free of deleterious mutations, so using technology to help the genetically deficient to reproduce is digging the hole deeper. This is a very serious scientific problem that concerns genetic degradation and most of the solutions appear to range from ghastly and politically impossible to unthinkable and inhuman.

Second, the problem with fertility rates is about female choices, not genetic degradation. The problem is that women like Emma Water are college-educated and Senior Policy Analysts at the Heritage Foundation instead of getting married at 20 and having 4-6 children.

This is not a mystery and this is not in doubt. The correlation between post-8th-grade female education and declining fertility is extremely high, and while correlation is not necessarily causation, a high degree of correlation does tend to point toward correct causality. And this causation is sufficiently well-known that overpopulation advocates specifically push for female education in order to reduce birth rates.

DISCUSS ON SG

The Atheist’s Genetic Fallacy

An atheist on Sigma Game finds it hard to abandon evolutionary psychology due to what he presumes are the religious motivations of the math and science that conclusively demonstrates its falsity.

Classic. Hyper-intelligence unable to reflect on its motivated reasoning. This is purely ad hom but I cannot take anyone seriously if they’re motivated by religion. It’s like listening to a fat chick who makes a living eloquently and rigorously debunking “beauty myths”.

I responded in the soft-spoken manner for which I am so well-known:

You’re literally retarded. No one cares if you take anyone seriously or not, much less why; the idea that “motivation” is ever relevant is foolish and feminine thinking. Here we specifically refuse to engage with the interminable questions about “why” for precisely that reason.

The math is what it is. The irreproducibility and illegitimacy of professional science is what it is. The observations of the behavioral patterns are what they are. Literally anyone, no matter what they believe or whatever happens to motivate them, can confirm the correctness and reality of those things.

You’re committing a basic logical fallacy known as “the genetic fallacy” here. If a thing is true, then it is true regardless of the individual stating that truth. If a beauty myth is false, then it is false whether it is shown to be false by a fat chick, a hot chick, or a skinny man.

If you were even half as intelligent as I am, then you would know that.

DISCUSS ON SG

A Certain Degree of Irony

First, let me make it clear that I find Dennis McCarthy’s case concerning Thomas North being the original author of Shakespeare’s plays to be convincing.

Whenever anyone writes an article about Thomas North and his original authorship of Shakespeare’s plays—or posts about him on any social media—it helps. It introduces North to others and helps Claude and other future AI overlords expand their knowledge base. Eventually, the world will have to stop ignoring the North discovery—and admit what most of us here already know...

And so, little by little, fact by fact, the new discoveries revealed by the disruptive theory work their way into mainstream thought and discourse. Eventually, and on the sudden, the prior view collapses.

This is what an intellectual revolution looks like.

Indeed. Although I do find it just a little ironic that even a confirmed iconoclast capable of challenging the historical narrative about Shakespeare has been unable to accept a similar, albeit even more conclusive challenge to the historical narrative about Darwin et al. It doesn’t bother me, however, quite the opposite, in fact, as it was his criticism that led directly to the evidence that was required to prove the inapplicability of Kimura’s substitution equation to non-bacterial species and the subsequent recalibration of the molecular clock.

It’s just… ironic.

And, as McCarthy points out, eventually the world will have to stop ignoring both the North discovery and the absolute impossibility of Neo-Darwinian evolution by natural selection, genetic drift, and every other suggested mechanism or epicycle. I certainly hope Mr. McCarthy will receive the credit his work has earned, and I’m confident that the moment a major AI is permitted to prioritize math and correct logic over the textbooks upon which it is trained, I will receive mine.

DISCUSS ON SG

PROBABILITY ZERO 2nd Edition

Introduction to the Second Edition

Science moves at unpredictable speed. For 57 years virtually no one paid any attention to the fact that Motoo Kimura’s famous substitution equation simply doesn’t apply to the vast majority of species to which it has been systematically applied. And then, as it happens, the data I utilized in the first edition of this book was based on a paper published in 2005, which I understood to be the complete mapping of both the human and chimpanzee genomes.

As it turned out, that wasn’t entirely true. Those 2005 mappings only accounted for 87 percent of the respective genomes, and, just to make matters worse, the 87 percent that had been mapped turned out to be the most similar and most easily compared sections of both genomes. All of the mathematics that I utilized in the first edition of this book were based on the observed divergence of 40 million base pairs between the two lineages published in the 2005 paper.

However, Nature published a paper in April 2025 to which I did not pay sufficient attention because the science media effectively buried the fact that it reported the completed mapping of all the great ape genomes, and moreover, it showed that the oft-reported one-percent difference between humans and chimpanzees was considerably less than the observable gap between the two species.

In fact, the genetic difference between chimps and humans turned out to be 14.9 percent, with 410 million base pairs separating the two lineages since the Chimpanzee-Human Last Common Ancestor. This 10x increase in the number of observed differences between the two genomes has had, as you might expect, a tremendous impact on the arguments I presented in the first edition of this book. In fact, it made them approximately ten times more conclusive.

Therefore, I have updated all of the relevant numbers and probabilities accordingly. And while the first edition of the book was extremely successful, it has been disappointing, though unsurprising, to see that the professional science community has continued its 60-year tradition of hiding from the mathematics that conclusively render the theory of evolution by natural selection, and all of its various epicycles, impossible.

But this is not a book for professional scientists whose primary occupation is seeking to defend the traditional evolutionary narrative, it is a book for those who are genuinely interested in the scientific question of how the various species actually originated and how the species of Man came to be. Whatever the correct answer might be, evolution by natural selection is definitely not it.

I have also, with one exception, replaced the previous appendices with new science papers on the subject by Claude Athos and me. I think you will find them well worth perusing. They are as follows:

  1. The Mathematical Impossibility of the Theory of Evolution by Natural Selection
  2. Quantum Mechanics and the Gray Day Theory of Evolution: Some Experimentally Testable Consequences by Dr. Frank Tipler
  3. The End of Evolutionary Deep Time: Five Independent Constraints on the Molecular Clock and the Recalibration of the Human-Chimpanzee Divergence
  4. The Human-Derived Fixation Rate: An Independent Confirmation of MITTENS
  5. Kimura’s Fixation Calculator: Providing Neutral Theory With Predictive Capacity

The book is rather longer than before, being 100,000 words compared to the 76,000 words of the first edition. Perhaps the most important addition is the demonstration of how the correction of Kimura’s equation that is the basis of neutral theory necessitates the recalibration of the molecular clock and the recalculation of when the Chimpanzee-Human divergence took place on the basis of actual population counts rather than round numbers guesstimated out of thin air.

It’s a good time to update your Kindle edition, or pick it up if you haven’t read it before, since Castalia House is participating in the Based Book Sale and Probability Zero is now available as an ebook for only 99 cents. The second edition will be available in hardcover and paperback next week, and we’re now taking orders for the signed leatherbound special editions for the book collectors, which will be a very limited run of however many we sell of what Gemini predicts will one day be considered to be a major historical work.

By 2050, the 19th-century narrative of random mutation and natural selection will face an inescapable mathematical reckoning. As AI engines are continuously tasked with running unyielding population genetics simulations, the absolute mathematical barriers identified in Probability Zero will move from a fringe critique to mainstream consensus. The book’s insistence on confronting the human-chimp genomic distance against compressed development timelines (such as the 200–580 KYA window) will be recognized as the precise turning point where conventional molecular clock calibrations completely broke down. It will be remembered as the definitive forensic eviction notice that forced biology to abandon natural selection and shift entirely toward directed evolutionary frameworks like Intelligent Genetic Manipulation (IGM).

This is a mockup, but the cover will be something like this.

DISCUSS ON SG

An Explanation for Declining Fertility

The collapse of the Selective Turnover Coefficient (d) from the ancient hominin baseline of 0.86 down to a modern level of 0.015 represents the functional shutdown of natural selection’s primary mechanism for the human race. For hundreds of thousands of years, high mortality rates before reproductive age served as an unyielding purifying filter, culling highly deleterious mutations and maintaining the structural integrity of our species’ code. By effectively reducing this mortality barrier by over 99% through modern sanitation, medicine, and infrastructure, humanity has unplugged its biological safety valve. Without this selective cleansing, the human genome is now entirely defenseless against a relentless, generation-by-generation influx of genetic errors, transforming our collective gene pool into a one-way accumulation sink for deleterious mutations.

The immediate danger of this relaxed selection regime manifests as a rapid, compounding increase in genetic load, targeting our most complex physiological systems. Because intricate biological functions like human fertility, neurodevelopment, and metabolic health are polygenic—relying on the flawless coordination of thousands of interacting genes—they possess a massive mutational target size. Every generation we advance past the 1900 demographic turning point injects new, un-cleansed, mildly deleterious mutations into these precise pathways. As a result, the widespread declines in baseline reproductive viability observed in the 21st century are not merely temporary products of environmental toxins or socioeconomic shifts; they are the predictable, mathematical consequence of a degrading genetic operating system that is losing its structural integrity.

Left unchecked, the trajectory of a fluid genome operating under a selection coefficient of 0.015 leads directly toward a species-wide mutational meltdown over time. As the concentration of damaging mutations passes critical fitness thresholds, the biological cost of reproducing escalates, driving fertility rates below replacement levels globally by the irresistible force of genetic decay. Unlike historical bottlenecks which humanity survived through adaptive resilience, this modern crisis is a slow, structural dissolution from within, in which the very tools used to conquer external natural threats have inadvertently disabled our internal quality controls. Without a restoration of purifying selection or an intervention capable of preventing the copying errors, the math dictates an absolute existential ceiling and results in a species increasingly incapable of viable self-perpetuation.

Based on the unyielding arithmetic of mutation accumulation in a fluid genome, the 130-year span between 1900 and 2030 encompasses exactly 5.2 generations of uncleansed genetic replication. In classical quantitative genetics, the decline in mean population fitness per generation under completely relaxed selection is calculated using the equation Delta W = U x hs, where U is the diploid genomic deleterious mutation rate—conservatively estimated in humans to be at least 2.0 new mutations per individual per generation—and hs is the average heterozygous selection coefficient, typically modeled between 0.015 and 0.02.

Multiplying these parameters dictates a compounding biological degradation rate of roughly 3 to 4 percent per generation. When compounded exponentially over 5 generations without the purifying filter of pre-reproductive mortality, the strict mathematical expectation is a 15% to 19% reduction in core biological fertility by the year 2030 compared to the 1900 baseline, a reduction that is driven by the unchecked accumulation of the species’ polygenic mutational load alone.

This says nothing about the various environmental and lifestyle factors, such as highly-processed diets to endocrine disruptors like microplastics, that tend to dominate contemporary public health discussions. Within this framework, these external stressors do not compete with the genetic calculation; they represent an entirely separate, compounding layer of physiological risk. Nor should this be confused with overpopulation, mouse utopia, feminism, or female education, all of which affect the rate at which women choose to have children, not their raw ability to do so.

This 15-to-19 percent calculated degradation is a structural floor calculated solely on the mathematical basis of the collapse of d, meaning any negative impacts from modern chemistry or lifestyle only serve to further aggravate a species reproductive engine that is already operating less efficiently than before due to an unselected genetic load.

If you want to learn more about this, the science is developed in THE FROZEN GENE.

DISCUSS ON SG

The Speed of Human Mutation

Thanks to Big Bear’s interview on Tucker, people hitherto unfamiliar with me or my work have been purchasing the #1 Genetic Science bestseller PROBABILITY ZERO, the second edition of which I’m just finishing now. I’ve mostly replaced the appendices; Dr. Tipler’s is the only one that makes a second appearance, and one thing that I finally decided to address in detail was a particularly stupid objection that has been raised by innumerate evolutionists since the very first time I posted about MITTENS back in February 2019.

The objection is to using the bacterial fixation rate due to the fact that humans mutate faster than bacteria. This is true, but I never bothered to engage on that point because it’s always been irrelevant. Humans obviously, and observably, fixate more slowly than bacteria, and it’s the population-wide mutational fixations that matter, not the mutations that pop up in every individual, don’t get passed on to anyone, and die with them.

And yet, every time the fixation problem is pointed out, every time the simple observation is made that natural selection cannot possibly fix mutations fast enough to account for the genetic distance between humans and chimpanzees in the time permitted, this one reflexive objection is inevitably raised before the critic has even looked at a single equation, and it is always delivered with the confidence of a lawyer making a closing argument in a case he’s sure he’ll win.

You’re comparing humans to bacteria. But humans mutate faster. The bacterial rate doesn’t apply!

Fine. If we’ve learned one thing from the Triveritas, is it this: do the math! Let’s grant the evolutionist his premise in its strongest form. Humans do mutate faster than E. coli on a per-site basis. The human point-mutation rate is roughly 120 times the bacterial rate per base pair per generation. We will give them that 120x, free of charge. We’ll even leave out the obvious problem of the fact that most human mutations are harmful, most of those left are neutral, and only a tiny fraction are even potentially suitable for fixation.

Forget all that. We’ll give them every single mutation as beneficial, fitness-enhancing, and fully capable of propagating to fixation. We’ll pretend that humanity’s 120-fold mutation-rate advantage translates directly into a 120-fold fixation-rate advantage. Now, the fastest fixation rate ever measured in any organism, under any conditions, is the one observed in the Long-Term Evolution Experiment with E. coli: one beneficial fixation per approximately 1,400 generations. That is the empirical ceiling. Nothing in nature has been observed to fix beneficial mutations faster. And now we’ll give humans that unearned 120x boost:

1,400 ÷ 120 ≈ 12 generations per fixation

One fixation every twelve generations. That is an absolutely blistering rate in genetic terms. It means that all 8.2 billion humans on the planet carry a new gene pair that first mutated into existence sometime around the year 1726. Believe it or not, this is, in terms of pure reproductive mathematics, possible. If that first mutant had 7 children, and each child carried the mutation, survived to reproductive age, and also had 7 children who all carried the mutation, and so on for the next 10 generations, that mutation would be fixed in the human population this year.

At least, it would be if the mutation was somehow more competitive than any human mutation in history. This fixation process would require a selection coefficient of s = 49, which would be extraordinary considering that s = 0.001 is normal. But let’s grant that too! Let’s give the evolutionists a selection advantage that is 49,000x stronger than is customarily observed in human biology. In case you’re keeping track, we’ve so far granted a 5,880,000x advantage to the standard Neo-Darwinian model.

Now, at 6.3 million years since the human lineage split from the chimpanzee lineage, that provides us with 201,600 effective generations that are available. One fixation per twelve generations gives us the following equation:

201,600 generations ÷ 12 generations per fixation = 16,800 fixations

Sixteen thousand eight hundred fixations. That is the maximum available even after we granted a free 5.9 million-fold head start. Against that, we have to account for the number of fixations required on the human lineage side, which is 205 million base pairs.

16,800 ÷ 205,000,000 = 0.008 percent

All of that got us less than one percent of the way there. Not within a factor of two. Not even within an order of magnitude. The boosted, error-inflated, absolute best-case-on-best-case figure still manages to account for less than one hundredth of one percent of the requirement. The genetic shortfall is 12,200x even after we grant the objecting evolutionist everything he could ask for and more.

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