Expansions of Price Equation for Viability and Fecundity Selection
Bibliographic record
Abstract
Abstract The Price equation offers a means of separating the portion of population change attributable to natural selection from the overall change. There is debate over the universality of the Price equation due to the belief that it makes simple assumptions. However, it should be noted that the definition of fitness is assumed within the Price equation. To account for populations subject to viability selection and fecundity selection during their life cycle, two expansions of the Price equation have been proposed based on a clear understanding of fitness. These expansions include three terms: a viability selection term, a fecundity selection term, and a mean value term that captures the difference between adults and their zygotes. Unlike the classic Price equation, which also has a mean value term capturing the difference between adults and their successful zygotes, the mean value term in our expansions will be zero in the absence of mutation, drift, and recombination. Furthermore, the individual-based simulation shows that our expansions can outperform the classic Price equation in capturing the average change in strategy for resource allocation. In summary, our expansions fully capture the effects of natural selection and separate them into viability and fecundity selection terms. Our expansions allows for a wider application of the Price equation, especially in the case where there is a trade-off between viability and fecundity selection.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".