INDIVIDUAL PHENOTYPE, KINSHIP, AND THE OCCURRENCE OF INBREEDING IN SONG SPARROWS
Bibliographic record
Abstract
Inbreeding load, a key parameter in evolutionary ecology, is frequently estimated by regressing fitness (or related traits) on inbreeding coefficient across population members. This approach assumes that inbreeding occurs randomly with respect to an individual's intrinsic ability to produce fit offspring; estimated loads might otherwise be biased by covariation between inbreeding and individual quality. This assumption, however, is rarely validated. We tested whether, in free-living song sparrows Melospiza melodia, an individual's observed kinship with its social mate (and hence the degree of inbreeding in which an individual participated) was correlated with specific phenotypic traits that are likely to indicate individual quality. Males (and to some extent females) that hatched earlier within their cohort, had shorter tarsi, bred earlier during their first year, or survived fewer years paired with more closely related mates and therefore produced relatively inbred offspring. These correlations arose because males with specific phenotypes were more closely related to the female population (and therefore more likely to pair with closer relatives under random pairing), and because males with specific phenotypes paired with closer relatives than expected. Such correlations could bias estimated inbreeding loads, and should be considered in quantitative genetic analyses of phenotypic variance in populations in which inbreeding occurs.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".