Looking at the math with real data: METHODS - DATA SOURCES:
We compiled age-specific mortality rates (5-year intervals, ages 0-85+) from high-quality vital statistics spanning 271 years. Historical periods used: Sweden 1751 (parish records following Human Mortality Database protocols; Wrigley & Schofield demographic reconstruction methods), England 1841 (census-based life tables), USA 1900-2022 (Death Registration Area transitioning to Social Security Administration cohort life tables). Age-specific fertility schedules derived from corresponding demographic databases (Coale-Trussell natural fertility models for historical periods, CDC/NCHS for modern USA). All periods have empirically-grounded mortality and fertility schedules, avoiding model life table projections. Sample sizes: Sweden 1751 n≈2.5M, modern USA n>300M, ensuring statistical robustness.
CONTEXT: We quantified how the demographic transition (1751-2022) altered two key evolutionary parameters: the demographic turnover coefficient d and the mutation fixation rate k.
DEMOGRAPHIC TURNOVER (d):
We calculated d = T × [∫ μ(x)l(x)v(x)dx / ∫ l(x)v(x)dx], where μ(x) is force of mortality, l(x) is survivorship, v(x) is reproductive value, and T is generation time. Using high-quality vital statistics (Sweden 1751 → USA 2022), d declined 72% (0.388 → 0.107).
Decomposition analysis revealed mortality decline explains ~100% of this reduction; fertility changes contributed negligibly. The 90-95% reduction in reproductive-age mortality (ages 15-30) overwhelmed any effects from changing fertility timing. This directly confirms the "relaxed selection" hypothesis: the demographic substrate for viability selection has collapsed.
MUTATION FIXATION RATE (k):
Under Wright-Fisher assumptions, k = μ. However, overlapping generations violate this: mutations from past cohorts exist at frequencies > 1/(2N), not all starting fresh at 1/(2N). We calculated k accounting for age structure, generation overlap (20-year reproductive span), and weighted parental contributions.
Result: k increased 67% (0.299μ → 0.500μ) from 1751 to 2022, approaching the Wright-Fisher expectation. Better survival to reproductive age (61% → 96%) drives this increase.
CORRELATION: k vs d
Pearson r = -0.991 (p < 0.001, R² = 0.983). These parameters are inverse sides of the demographic transition: improved survival simultaneously increases mutation transmission efficiency (k↑) while decreasing mortality-based turnover (d↓).
The parallels between AI and humans is illuminating especially since you can do many more interactions and iterations with AI than you can with humans before they break down.
The idea that the planet has a singular climate that can be modeled without reference to its many, many regional climates is... A non-interrogatable premise of established science-?
Looking at the math with real data: METHODS - DATA SOURCES:
We compiled age-specific mortality rates (5-year intervals, ages 0-85+) from high-quality vital statistics spanning 271 years. Historical periods used: Sweden 1751 (parish records following Human Mortality Database protocols; Wrigley & Schofield demographic reconstruction methods), England 1841 (census-based life tables), USA 1900-2022 (Death Registration Area transitioning to Social Security Administration cohort life tables). Age-specific fertility schedules derived from corresponding demographic databases (Coale-Trussell natural fertility models for historical periods, CDC/NCHS for modern USA). All periods have empirically-grounded mortality and fertility schedules, avoiding model life table projections. Sample sizes: Sweden 1751 n≈2.5M, modern USA n>300M, ensuring statistical robustness.
CONTEXT: We quantified how the demographic transition (1751-2022) altered two key evolutionary parameters: the demographic turnover coefficient d and the mutation fixation rate k.
DEMOGRAPHIC TURNOVER (d):
We calculated d = T × [∫ μ(x)l(x)v(x)dx / ∫ l(x)v(x)dx], where μ(x) is force of mortality, l(x) is survivorship, v(x) is reproductive value, and T is generation time. Using high-quality vital statistics (Sweden 1751 → USA 2022), d declined 72% (0.388 → 0.107).
Decomposition analysis revealed mortality decline explains ~100% of this reduction; fertility changes contributed negligibly. The 90-95% reduction in reproductive-age mortality (ages 15-30) overwhelmed any effects from changing fertility timing. This directly confirms the "relaxed selection" hypothesis: the demographic substrate for viability selection has collapsed.
MUTATION FIXATION RATE (k):
Under Wright-Fisher assumptions, k = μ. However, overlapping generations violate this: mutations from past cohorts exist at frequencies > 1/(2N), not all starting fresh at 1/(2N). We calculated k accounting for age structure, generation overlap (20-year reproductive span), and weighted parental contributions.
Result: k increased 67% (0.299μ → 0.500μ) from 1751 to 2022, approaching the Wright-Fisher expectation. Better survival to reproductive age (61% → 96%) drives this increase.
CORRELATION: k vs d
Pearson r = -0.991 (p < 0.001, R² = 0.983). These parameters are inverse sides of the demographic transition: improved survival simultaneously increases mutation transmission efficiency (k↑) while decreasing mortality-based turnover (d↓).
The parallels between AI and humans is illuminating especially since you can do many more interactions and iterations with AI than you can with humans before they break down.
So Deepseek is akin to the DEI regulator who approves research grants. Its utility is providing the current consensus so it can be shredded.
Bravo. Bravissimo.
Also spotted something new: "climate denial"
The idea that the planet has a singular climate that can be modeled without reference to its many, many regional climates is... A non-interrogatable premise of established science-?
Pure quill Woke.
(Updated to fix the backwards sentence)