Offspring sex ratios in subcolonies of Audouin's gull, <i>Larus audouinii</i>, with differential breeding performance
Bibliographic record
Abstract
At the Ebro River delta colony in the western Mediterranean Sea, Audouin's gull, Larus audouinii, breeds in discrete aggregations called subcolonies, which showed strong differences in breeding parameters such as egg volume or breeding success. Egg parameters (such as size of both eggs and clutches) are strongly influenced by food availability. As all subcolonies are in the same area, differences in egg parameters might reflect different individuals' foraging efficiency. We measured mean clutch volumes in different subcolonies and chose those subcolonies that showed the greatest differences in this measure, which should indicate differences in parental body condition. Between these subcolonies we would expect, in turn, differences in offspring sex ratios. We took blood samples at hatching and fledging from chicks at these subcolonies and compared offspring sex ratios by means of molecular sexing. The proportions of young breeders differed between these subcolonies, and the subcolony with the greater proportion of young breeders produced smaller eggs and had lower breeding success. However, we did not detect any bias in progeny sex ratio, which probably indicates that if parental condition is not extremely reduced, selective pressures are insufficient to overcome the constraints imposed by Mendelian segregation of chromosomes.
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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 | 0.000 |
| 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 | 0.000 | 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".