Author: VD

Neither Mathews nor any of her supporting cast of PhDs understand the basic concepts of math. For some reason, biologists are totally unable to understand the concept of “an average” or “an average rate” or even grasp the fact that if something happens multiple times over a period of time, then an average rate for that activity or process must necessarily exist.

(45:52) Maddie brings up another problem, and this one has to do with Vox’s complete lack of basic foundation in biology. So, Zach, the fundamental flaw in his logic, and this is the like what he thinks should be true of biology that simply is not, right, is he thinks that there should be some universal constant that represents fixation rate that can be applied to any species, any population at any point in time for any reason. And I make this claim out of his book, which presumably is why evolutionary biologists have operated for over 50 years without ever managing to establish the fundamental rate constants of their field. Right? He assumes that there are fundamental rate constants.

The part where he’s asking people like what is the average rate of evolution? And so that’s what he claims started the he’s he claims what started him down this entire rabbit hole is that he was asking people what is the average rate of fixation and nobody was giving him a straightforward answer and so that’s what he said well obviously they’ve all missed this or no I think what he says is they they must all not know how averages work not. Maybe my question is ill-formed or maybe I don’t understand the field at all. So to be to be clear, this is this is what he this is how he continues, right?

After saying which presumably is why evolutionary biologists have operated for over 150 years without ever managing to establish the fundamental rate constants of their field. They will tell you that evolution happens, can describe what evolution supposedly produces, can spin narratives about why certain features were selected, and will even tell you that there are no other possible alternatives to evolution by natural selection. But they cannot tell you how fast any of this occurred. And when you ask the biologists don’t say we’re not sure, or estimates vary, or that’s an active area of research. They genuinely don’t understand the question.

So let me give an example of what this made me think of out of weather that I think is pretty tangible to most folks. So if you asked me what’s a good average wind speed for planet Earth, right? I would tell you 10 meters per second because that’s like a solid order of magnitude. That’s normal, right? But what Vox is doing is saying that oh well well one that like um he would he would be saying that oh it’s 10 meters per second uh then explain how a hurricane exists right it’s like well because the winds can change right or like and so this idea that like yeah I can give you a characteristic number right that’s like if you were to go outside on a random day under average normal conditions Maybe is again order of magnitude what you’re going to find.

There just so much variance that it’s like an average rate doesn’t, it’s like meaningless.

Yeah. But but telling you that is meaningless. It’s literally only useful for like order of magnitude when can we ignore certain terms in Navier Stokes, right? Like um and so yeah. So that that’s what I feel like he’s doing. I feel like this is the equivalent of him being like, “Oh, an average wind speed is 10 meters per second. Stupid meteorologists don’t know how tornadoes work. Well, it’s not it’s not as though rates for for given populations can’t be derived either. It’s not like we can’t figure out mutation rates for different groups of organisms.

This level of retardery from scientists with PhDs is genuinely painful to read, because they’re confidently asserting that the very concept they just spent the previous twenty minutes using — average fixation time — doesn’t exist. I’m not asking about some sort of universal biological constant, I am asking what is the average rate of this process taking place over time?

You said you drove the car from New York to LA last week. Fine. How fast did you drive? That is not a question about a universal automotive constant.

What do they imagine 4N_e is? It’s the average time to fixation for a neutral mutation. It’s right there in every population genetics textbook ever written. What do they think (2/s)·ln(2N_e·s) is? It’s the average time to fixation for a beneficial mutation with selection coefficient s. Kimura derived these. They are the expected values — the average rates — of stochastic processes. The entire field of population genetics is built on computing expected values for fixation times, substitution rates, and allele frequency trajectories. That’s exactly what the field does.

Their wind speed analogy is particularly self-defeating. Meteorologists absolutely use average wind speeds. They’re essential to climate modeling, building codes, wind energy engineering, and aviation. The existence of hurricanes doesn’t make the average meaningless, it just means the distribution has fat tails, which any competent scientist accounts for rather than throwing up their hands and declaring all averages to be useless. And if a meteorologist told you “the concept of average wind speed is meaningless,” you’d rightly question whether they understood their own field.

And the crazy thing is that these six ignoramuses just finished citing specific fixation times from Kimura’s formulas to argue that recombination and standing variation make human evolution faster than bacterial evolution observed in the LTEE. Those numbers they were using? Those were averages. They were using average fixation times to argue that average fixation times don’t exist. They didn’t notice the contradiction because they don’t understand the math well enough to recognize that the formulas they’re citing are the rate constants they’re claiming can’t possibly be established.

Every time you observe something taking place over time, it ABSOLUTELY WILL produce an average rate. This isn’t a biology claim. It’s arithmetic. If X events occur over Y time, then X/Y is the average rate. It doesn’t matter if the variance is enormous. It doesn’t matter if the rate fluctuates wildly. It doesn’t matter if some periods see bursts and others see nothing. The average rate exists as a mathematical certainty from the moment you have a count and a duration.

The LTEE has a count: observed fixations per population. It has a duration: generations elapsed. Divide one by the other. That’s the average rate. It’s not a philosophical position. It’s not a modeling assumption. It’s division. The human-chimp divergence has a count: 398,673,272 base-pairs observed between the two lineages. It has a duration: approximately 6.3 million years. Divide one by the other. That’s the average rate of post-CHLCA human evolution: 63.281 base-pairs per year.

We get different rates for the human divergences from each of the six other great apes ranging from 37.8 to 99.4 bp/year. So we add them all together, divide by six, and we reach an average rate of human evolution based on six different speciations across at least 60,500,000 years and the detailed analysis of 25,302,032,866 base pairs:

62.7 base pairs per year, or 1,958.81 base pairs per effective generation.

You can argue about the precision of the inputs, you can argue about which lineage each of those divergent base pairs should be assigned to, but you cannot argue that the quotient doesn’t exist or has no meaning. And it’s interesting to observe the similarity between the chimp average and the ape average holds despite the difference of 9.6 million years between the most distant and the most recent speciations. But regardless, that’s the speed at which evolution by natural selection, neutral drift, and relictation has to take place, assuming it took place at all and is responsible for all of the observed divergences.

What the Evo-Avengers are actually doing without realizing they’re doing it is confusing “the average rate varies across species and conditions” with “the average rate doesn’t exist and is meaningless.” Those are completely different statements. The first one is trivially true and is exactly what Probability Zero is based upon and what I have taken into account here. The second one is mathematically illiterate. And the fact that multiple PhDs in biology and meteorology and population genetics are publicly declaring the second while imagining they are asserting the first tells you everything you need to know about their total inability to comprehend the math or even try to address any of the unavoidable problems for evolution it creates.

Observe that none of this had anything to do with the LTEE about which they were complaining. None of it. Not a single number in that calculation came from Lenski. No bacterial fixation rates. No clonal populations. No E. coli generation times. No extrapolation from prokaryotes to eukaryotes. Every single input is from the mainstream scientific literature:

• The generation time is the science’s standard 25-year human estimate
• The base pair differences come from the science’s mappings of the seven Great Ape genomes.
• The divergence times come from the science’s molecular clocks and fossil calibrations

The only innovation on my part is to provide the necessary time-weighted correction for overlapping generations, which doesn’t fundamentally change anything, it merely makes the average rate a little more accurate and applicable to real-world humanity.

DISCUSS ON SG

This may actually be the most obviously retarded section of the Evo-Avenger’s incompetent attempt to refute MITTENS, because they are, quite seriously, arguing that humans and chimpanzees can fixate mutations across their entire populations in the wild faster than bacteria can in the long-term Lenski experiment, otherwise known as the LTEE, due to polymorphism and recombination. This is what happens when you appeal blindly to mechanisms rather than working through the relevant calculations.

(40:47) I don’t know why he wrote a book right because he references the idea that the fixation rate um from bacteria when applied to human populations um uh doesn’t work uh when you just take an average of the number of fixations per so he takes a really simple average um out of the Linsky experiment right and then applies that number as a fixation rate to the human population assuming that all mutations are happening in series. He claims that he’s not assuming that but uh just says nuh-uh like there’s no math to prove that he’s not right. Um and like so like he has one claim and wrote a whole book and never justifies the claim, right? He never proves the methodology which is what’s driving me absolutely bonkers.

So, like this is poorly written even if the idea was sound. Um, and that’s my that’s my hot take here at the top. Before we hear from Zach Sebater, who has a PhD in biology with doctoral and post-doal experience in viral evolution says, “The first thing that caught my attention in Day’s work is that he uses data from bacterial experiments to draw conclusions on human evolution. One major difference, I think, is that E.coli coli reproduced mostly clonally while humans reproduce 100% sexually and KImura’s simple model doesn’t take recombination into account whereas more elaborate models do and in fact recombination does accelerate the rate of beneficial mutations through several means this is reaffirmed by Garrett Ferguson below, who has no science accreditation but noticed it all the same, so I guess if I can jump in the the issue with using even conceptually the Lenski long-term evolution experiment for a fixation rate is that the Lenski long-term experiment begins with a bacterial population that is clonal and has no diversity.

Um, and so because of that, every single mutation that has to occur is novel. Um, but when you think about humans and chimps splitting, they don’t have zero diversity. They’re not building up brand new mutations, right? We have a level of polymorphism that more or less represents mutation drift equilibrium. And at that at that point, we can calculate the rate of fixation, right? Um, and so he’s getting an exorbitantly large number even for bacteria simply because he’s starting with a population that has zero polymorphism, right? And so you have to build up that polymorphism through time. And that’s just not characteristic of how any natural population works.

Allow me to try to simplify a little bit what Zach is saying here. In Lensky long-term evolution experiment, the starting populations of bacteria were clonal, which is to say they were identical to one another. They were clones. Every bacteria is genetically identical to its neighbor, which means their genetic diversity was zero. How many natural populations look like that?
Now, there is a function here because by knowing the starting condition of all of your populations, and by having them all equal, you’re controlling for a lot, and you can more easily track the changes that do occur over time in these lineages. But because there’s no polymorphism, which is just our fancy word here for variation, it takes way longer for novelty to actually generate.

Let’s compare two hypothetical situations. In both situations, we have 12 baggies. And in each one of those baggies, there are 10 randomly selected Scrabble tiles. In situation one, each of those 12 baggies contains the same random 10 letters. Whereas in the other situation, each of the 12 baggies contains a different 10 randomly selected Scrabble tiles. Now, let’s pretend that every week every single one of these baggies gets one new randomly selected Scrabble tile added to it. The word making potential of these two situations is not equal because situation two has far more letters to choose from. And that’s right out the gate and moving forward until all of the baggies have the same general representation of letters in them.

Day is taking situation one there and applying it to the human chimpanzeee divergence which is inappropriate because we aren’t clonal. When the population of measene apes split into the lineages that would become humans and chimpanzees or the hominins and the pannins, it wasn’t clonal. All of those apes weren’t clones of each other. It was diverse, as were the lineages that came from it. So already the use of the Lenski long-term evolution experiment here is just wildly inappropriate, and there’s no fixing that.

Let’s keep in mind that they are seriously arguing, with a straight face and on the basis of their scientific credentials, that humans fixate mutations faster than bacteria in terms of generations per fixation. That is their actual position; don’t forget that.

The polymorphism objection sounds legitimate until you think about what standing variation is. It’s not a warehouse of pre-adapted beneficial alleles waiting for their moment. It’s mostly neutral variation, which is why it’s still segregating. If those variants were beneficial, they’d have already swept to fixation. If they were deleterious, purifying selection would have removed them. What mutation-drift equilibrium gives you is a population full of genetic noise at low frequencies, not a head start on adaptation.

The Scrabble analogy is rather useful for making this point: having twelve bags of random letters doesn’t help you spell words any faster, because the rate-limiting step was never “do you have enough letters?” It’s “how quickly does selection find and fix a combination that works?” That’s governed by s and Ne regardless of how much neutral junk is lying around. Standing variation can reduce the waiting time for a beneficial allele to appear, since one might already be segregating at low frequency. But it cannot reduce the fixation time once selection begins acting on it, which is still (2/s)·ln(2Ne·s) for the beneficial case and 4Ne for the neutral case. And the average s for mutations that actually fix in humans is approximately 10⁻⁴ and is completely unaffected by how much polymorphism the population started with.

There’s a deeper problem the polymorphism argument doesn’t address: even if you grant the one-time head start, it’s one-time. Once those standing beneficial variants fix or are lost, the population is back to waiting for new mutations, and now it’s producing new mutations at a rate roughly 30,000 times slower in real time. The LTEE’s observed fixation rate already includes the waiting time for new mutations to arise in a clonal population, so the critics are correct that it captures a worst-case scenario for mutation supply. But correcting for that makes the human situation worse, not better, because you’ve now isolated the fixation-time component, which scales with Ne and is inversely proportional to s, and both of those parameters are unfavorable in humans compared to E. coli.

So polymorphism and an appeal to ancestral mutations doesn’t help the evolutionists. Now let’s look at recombination, which is the more serious objection since it is, by nature, definitely applicable to humans and chimps and definitely not accounted for in the LTEE. In order to point out how mainstream the basic concept is, consider how Gemini describes the standard model as utilized by population geneticists:

Why Recombination Speeds Up Selection

In populations without recombination (or in regions of low recombination), genomes are inherited as indivisible blocks. This causes two major bottlenecks known as Hill-Robertson interference:

1. Clonal Interference: If two different beneficial mutations happen in different individuals simultaneously, they compete with each other rather than combining. One usually destroys the other, slowing down adaptation.
2. Genetic Hitchhiking & Background Selection: A beneficial mutation might accidentally arise on a chromosome loaded with harmful mutations, dragging it down, or it might get blocked because it’s linked to a competing sweep.

Recombination solves this traffic jam. It acts like a genetic shuffle button, allowing beneficial mutations from different lineages to combine into a single “super-genome” and uncoupling beneficial alleles from deleterious ones. This allows selection to act with maximum efficiency.

Fixation Times for Beneficial Mutations (Enabled by Recombination)

When recombination successfully prevents interference, the average time to fixation for a beneficial mutation with a selection coefficient ($s$) is governed by the deterministic-stochastic hybrid formula:

$$\bar{T}_{\text{fix}} \approx \frac{2}{s} \ln(2N_e s)$$

Because selection scales logarithmically with population size ($N_e$), larger populations actually fix beneficial mutations faster relative to their size than neutral mutations do, provided recombination is active.

Here is what the average generation-to-fixation timeline looks like for a beneficial mutation under two different selection pressures (moderate selection $s = 0.01$ vs. weak selection $s = 0.001$):

Instead of taking hundreds of thousands or millions of generations (as neutral mutations do via pure drift), a beneficial mutation aided by recombination can sweep through an entire population of 1 million people in fewer than 2,000 generations (roughly 50,000 years)—transforming evolution from a sluggish crawl into an efficient mechanism for adaptation.

Problem solved, right? Well, it probably won’t escape your attention that a) even 1,060 generations per fixation isn’t anywhere nearly fast enough or b) this only applies to the small percentage of the human-chimp divergence that is selected. But none of that matters anyhow, because we don’t need to rely upon population genetics theories about selection strength (s) to give us a hypothetical range, we have empirical evidence from the literature to plug into those standard formulas.

Tataru et al. estimated that less than 0.5% of new amino acid mutations are beneficial, with a scaled selection strength of N_e·s ≈ 12. For human N_e ≈ 10,000, that gives s+ ≈ 0.0012 for the beneficial mutations that do arise. The direct McDonald-Kreitman estimate of α — the proportion of amino acid substitutions driven by positive selection — is approximately 12.5% in hominids, with the remainder being neutral or nearly neutral.

About 30% of amino-acid-changing mutations in humans have effects less than about 10⁻⁵ — effectively neutral — and these are the ones that predominantly fix by drift.

The weighted average for a fixed substitution: 87.5% of fixations are neutral with s ≈ 0, and 12.5% are adaptive at s ≈ 0.0012:

0.875 × 0 + 0.125 × 0.0012 = 1.5 × 10⁻⁴

s ≈ 0.00015.

That is a full two orders of magnitude below the “moderate selection” case that produced the theoretical 1,060-generation fixation time that wasn’t fast enough anyhow.

In sum, humans do not fixate faster than bacteria, measured in time or in generations, and recombination cannot sufficiently reduce time to fixation to compensate for the slower process of fixation in humans. And all of this is before taking overlapping generations into account. Incorporating d means the 14,648-generation fixation time at the empirical s for N_e = 10,000 becomes roughly 18,310 generations. The neutral baseline of 40,000 becomes 50,000. And the weighted average across all fixations, where 87.5% are neutral, gets even worse.

Furthermore: “Prokaryotes appear to be much more efficient than eukaryotes at promoting simple to moderately complex molecular adaptations.” – Dr. Michael Lynch, author of The Origins of Genome Architecture

This is Lynch undermining the objection to the LTEE extrapolation objection. They argued I can’t apply bacterial fixation rates to humans because bacteria are clonal and slow. Lynch, who is one of the most respected population geneticists in the field, has stated that bacteria are actually faster and more efficient at molecular adaptation than eukaryotes like humans, precisely because of their population size advantage.

Which is why I utilized the LTEE to demonstrate the applicability of MITTENS in the first place. If it doesn’t work for bacteria, it definitely doesn’t work for chimps or humans.

DISCUSS ON SG