Data from: Is biasing offspring sex ratio adaptive? a test of Fisher’s principle across multiple generations of a wild mammal in a fluctuating environment
Bibliographic record
Abstract
Fisher’s principle explains that population sex ratio in sexually reproducing organisms is maintained at 1:1 due to negative frequency-dependent selection, such that individuals of the rare sex realize greater reproductive opportunity than individuals of the more common sex until equilibrium is reached. If biasing offspring sex ratio towards the rare sex is adaptive, individuals that do so should have a higher number of grandoffspring. In a wild population of North American red squirrels (Tamiasciurus hudsonicus) that experiences fluctuations in resource abundance and population density, we show that overall across 26 years, the secondary sex ratio was 1:1; however, stretches of years during which adult sex ratio was biased did not yield offspring sex ratios biased towards the rare sex. Females that had litters biased towards the rare sex did not have more grandoffspring. Critically, adult sex ratio was not temporally autocorrelated across years, thus the population sex ratio experienced by parents was independent of the population sex ratio experienced by their offspring by the time of their primiparity. Expected fitness benefits of biasing offspring sex ratio may be masked or negated by fluctuating environments across years, which limit the predictive value of the current sex ratio.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.042 | 0.038 |
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".