Bibliographic record
Abstract
Abstract Forces that determine the allele frequencies in natural populations include genetic drift, natural selection, migration and mutation. A balance of opposing forces can, in some cases, cause allele frequencies to approach a stationary distribution over time. The form of this distribution is not influenced by initial allele frequencies, but instead is determined by the relative magnitudes of different evolutionary forces. Statistical distributions are presented for the stationary allele frequencies under several simple population genetic models (including the k alleles symmetrical mutation model and the Wright island model of migration). In addition, the sampling distribution of allele counts under these models are described. The latter is useful when using genetic marker data to estimate population parameters. A population is at genetic equilibrium if evolutionary forces have persisted long enough for a population to have reached the stationary distribution, this is not often the case in nature. Key Concepts: If evolutionary forces such as natural selection, genetic drift and migration remain constant allele frequencies may reach to a stationary distribution. Because evolutionary forces are often changing on a relatively short time‐scale the allele frequencies in many populations will not be at equilibrium. Statistics based on parameters of stationary distributions may provide a useful summary of the genetic variation in populations that are not at equilibrium. Simulation methods can be used to study allele frequency distributions for more complex models. Modern approaches focus on nonstationary sampling distributions derived using the coalescent process model.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.007 |
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".