Heterozygote Advantage in a Finite Population: Black Color in Wolves
Bibliographic record
Abstract
There is a striking color polymorphism for wolves in the Yellowstone National Park where approximately half the wolves are black. The genetic basis for this polymorphism is known, and fitnesses of the genotypes are estimated. These estimates suggest that there is strong heterozygote advantage but substantial asymmetry in the fitness differences of the 2 homozygotes. Theoretically, such fitnesses in a finite population are thought to reduce genetic variation at least as fast as if there were no selection at all. Because the color polymorphism has remained at about the same frequency for 17 years, about 4 generations, we investigated whether this was consistent with the theoretical predictions. Counter to this general expectation of loss, given the initial frequency of black wolves, the theoretical expectation in this case was found to be that the frequency would only decline slowly over time. For example, if the effective population size is 20, then the expected black allele frequency after 4 generations would be 0.191, somewhat less than the observed value of 0.237. However, nearly 30% of the time the expected frequency is 0.25 or greater, consistent with the contemporary observed frequency. In other words and in contrast to general theoretical predictions, because of the short period of time in evolutionary terms and the relatively weak selection at low frequencies, the observed variation and the predicted theoretical variation are not inconsistent.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".