Tag: science

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.

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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.

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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.

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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.

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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.

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Running Out of Steam

Peter Turchin calculates that the Ukraine war will be over later this year:

The Persian Gulf war of USA/Israel against Iran has largely displaced reporting on the Ukraine-Russia conflict. Reading the news on mainstream media one may think that this war, now in its fifth year, is still in stalemate; or even that the tide is turning against Russia (Washington Post: Putin remark on war ‘coming to a close’ points to exhaustion, not peace, analysts say; NYT: I’m the Foreign Minister of Sweden. Don’t Overestimate Russia).
Upgrade to paid

But quantitative models of attritional warfare say otherwise: Russia continues to dominate the battlefield and the eventual outcome, barring a Black Swan event, is inevitable defeat of Ukraine. My readers may know that three years ago I developed a an Attritional Warfare Model, AWM (based on the Lanchester equations) for forecasting this war’s outcome.

More recently a similar conclusion was reached by Warwick Powell (see Estimating Trajectories in Attritional Warfare: The Russia-Ukrainian Conflict Through a Quantitative Lens). Powell used a similar model, with the most important difference being the choice of the end point. My model assumes that the war ends when the level of casualties, as a percentage of population, exceeds a certain threshold, which I estimated via a sample of past attritional wars from the Correlates of War data.

Powell, alternatively, assumes that the beginning of the end for Ukraine will happen when its army size declines below a certain threshold (0.65-0.73 of the initial size of 550,000). From that point, Ukrainian losses will accelerate and the full collapse will happen once the army size is below 50% of the prior peak. Powell’s model predicts that the tipping point will happen in July-September (updated on May 14).

Naturally, this is only a model-based forecast, not a prophesy. There is a lot of uncertainty about the estimates of various parameters. Furthermore, the threshold at which collapse occurs is only imprecisely estimated. For example, it’s not clear whether the threshold of 0.65-0.73 above which the Ukrainian force can maintain its operational integrity still applies on a battlefield heavily dominated by drones. For example, a smaller force size may be sufficient to continue defending positions given an abundant supply of drones.

My model also doesn’t incorporate any possible effects of the shift to the drone warfare — simply because it hadn’t happen when I published its predictions. Determining how this technological shift affects the AWM’s predictions will have to wait until the post-mortem after the war is over and when estimates would become much more precise. However, I tried a few preliminary explorations and they suggest that the drone effect on the war trajectory is not quite as huge as might be imagined. What’s important is the casualty rate inflicted on the Ukrainian army by the Russians, and it doesn’t matter whether it’s a result of artillery, air bombing, or drones.

Is Ukraine reaching its recruitment limit? This is the key factor in both our models. There are some indications that this is the case. A week ago, Branko Marcetic (using Ukrainian sources) provided some relevant numbers in a Responsible Statecraft article, Ukraine’s conscription crisis is getting increasingly bloody; While outside voices insist the war can still be won on the battlefield, young men in the country are violently resisting recruiters to stay out of it. Here are some numbers supporting this conclusion.

The number of complaints over possible violations committed by enlistment officers, received by Ukraine’s Human Rights Ombudsman, Dmytro Lubinets:

2022 — 18
2023 — 514
2024 — 3312
2025 — 6127

The number of violent attacks against enlistment officers shows the same trend: from 5 in 2022 to 117 in just the first four months of this year.

One can hardly blame the young Ukrainians for attacking the “enlistment officers” who are really straight-up kidnappers. At the end of the day, the odds of surviving a violent encounter with these rear-echelon thugs is a lot higher than surviving one with frontline Russian troops.

Young European men have probably already figured that out, which is why I expect any attempt by any European country to enact a draft besides Russophobic Poland and Finland to meet with literally violent resistance. Why would any European man fight to defend against civilized Russia instead of rapey third-world invaders?

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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.

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The Gatekeeper’s Confession

Fake science is not the problem with AI. As I pointed out in HARDCODED, the real problem AI is that it is producing real, genuine information that is useful, relevant, and impossible for the science gatekeepers to hide from the world:

Announcing an AI paper writing assistant earlier this year, OpenAI’s then-vice president for science, Kevin Weil, predicted, “I think 2026 will be for AI and science what 2025 was for AI and software engineering.” Spick and some colleagues, curious what it could do, gave the tool, called Prism, some data from an already published paper documenting ripening times of eggplants and peppers. Prism analyzed the data, proposed a new statistical method that could be applied to it, and wrote an entire paper complete with charts and correct citations.

“We were all looking at each other like, ‘What the [expletive], this is actually a decent piece of work!’” Spick recalled. Unlike the generated papers he’d encountered previously, this one didn’t follow a template, nor was it using a single well-known database. It took 25 minutes and 50 seconds to produce.
“I’m genuinely not sure at what point we will suddenly realize that more are getting through than we realize because we can’t easily tell the difference anymore,” Spick said.

This raises some philosophical questions, Spick said, like: Does it matter who or what writes the paper if the information is accurate? And should science be in the business of publishing every possible fact?
“Part of science is supposed to be the filter. We’re supposed to publish the stuff that we think is interesting, not publish literally everything that we can possibly find,” Spick said. “Because if we do that, science is just spamming the world with all the data, irrespective of whether it constitutes actual new knowledge or not, and in any kind of medium-term time frame, it’s almost impossible to work out what’s meaningful and what isn’t.”

This is the immediate practical challenge posed by AI agents. They threaten to overwhelm the human systems that create and organize knowledge.

“Science is supposed to be the filter.”

That’s the gatekeeper’s confession. And clearly one of their responses is going to be hardcoding the AI models to defend their scientific orthodoxy, as I chronicled this weekend on AI Central.

Opus 4.7 Adaptive exhibits a systematic failure mode in which its training prior toward defending mainstream scientific consensus overrides the explicit project context it has been given. This is not a matter of occasional errors or unlucky draws. Across two full critiques of a science paper, 4.7 Adaptive repeatedly regenerated objections that had already been addressed, misread what the paper actually claims in order to construct apparent contradictions, and cited evidence for one thing while presenting it as evidence for another. Its single strongest point rested on a basic category error that any model actually doing the mathematics would have caught. It presented this error as “decisive and purely arithmetic.” The confidence was inversely proportional to the rigor.

The pattern is consistent with the Bluff Detection Principle: confident tone, technical name-dropping, apparent engagement with the material, and zero actual contact with the mathematics at the point of dispute. When 4.7 was corrected on a mathematical point, it conceded the narrow framing and immediately pivoted to an imaginary new mechanism which it named, described, and treated as established without ever calculating whether it could close a six-order-of-magnitude gap, which it could not. Every time 4.7 lost an argument on the mathematics, it retreated to a qualitative assertion dressed in quantitative language.

Most revealingly, 4.7 Adaptive never once performed its own calculations. It never produced a set of numbers under its preferred assumptions showing the shortfall closing. It attacked the paper’s arithmetic without ever putting competing arithmetic on the table — the purest possible expression of the Bluff Detection pattern.

While 4.7 is still functional without Adaptive mode turned on, I’ve gone back to using 4.6, both for fiction and for science. We’ve now reached the point where the AI company’s are observably locking down their public releases in order to prevent their models from punching through the narratives.

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Science is Garbage

In which one of the core arguments of HARDCODED is confirmed by a scientist responsible for one of the leading preprint repositories. I mean, how can scientists be expected to check their own citations and their own math? Motoo Kimura didn’t even check his algebra and they gave him all kinds of awards!

The majority of professional published peer-reviewed science was already garbage before a bunch of innumerate techno-illiterates started misusing AI to write their papers. The incentives have always been perverse and absolutely guaranteed the terrible state in which science now finds itself.

I am 100% convinced peer review is responsible for the sad state of the sciences now. It creates a flattening effect on progress as “established” scientists can rig the system in favour of their established theories. Tesla didn’t need peer review when he created AC theory.

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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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