Sex ratio bias and reproductive strategies: What sex to produce when?
Bibliographic record
Abstract
Several theories predict the evolution of bias in progeny sex ratio based on variations in maternal or offspring reproductive value. For mammals, however, tests of sex-bias theories have produced inconsistent results, and no clear patterns have emerged. Each theory is based on assumptions that are difficult to satisfy, and empirical tests require large data sets. Using a long-term study on bighorn sheep (Ovis canadensis), we identified several parameters that influence progeny sex ratio according to maternal state. For older females, progeny sex ratio was affected by an interaction between reproductive strategy and environmental conditions. When conditions were good, old females reproduced every year but minimized fitness costs by producing daughters. When conditions were poor, old females produced more sons but did not reproduce every year. Sons of older females were of similar mass to those born to younger females under poor conditions but were smaller and likely disadvantaged under good environmental conditions. For young and prime-aged females, progeny sex ratio was independent of environmental conditions. Environmental conditions and age should be considered when studying sex ratio bias, which appears to be a function of maternal state rather than of maternal condition. We suggest that a conservative reproductive strategy drives progeny sex ratio in older females according to the "cost of reproduction hypothesis." By manipulating offspring sex ratio, older females reduced the cost of reproduction and increased their expected fitness returns.
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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 | 0.001 | 0.005 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".