Factors affecting individual variation in nest-defense intensity in colonially breeding Black-tailed Gulls (<i>Larus crassirostris</i>)
Bibliographic record
Abstract
The physiological state of parent birds combined with the value of their clutch may affect the intensity of their nest defense. In colonially breeding birds, nest-defense intensity may also be affected by the behavior of neighbors. We investigated individual variation in the nest-defense intensity among colonial Black-tailed Gulls ( Larus crassirostris Vieillot, 1818) over 2 years. Only 30%–40% of males attacked a decoy of an egg predator (Large-billed Crow ( Corvus macrorhynchos Wagler, 1827)), and the other males and females rarely attacked. Males attacking the decoy had higher levels of plasma testosterone than males that did not attack. Each male’s, but not female’s, nest-defense intensity was consistent throughout the incubation period and also across years. The intensity was not related to egg-laying date, clutch size, or age of offspring. The intensity was likely to be higher when individuals had one or more neighbors, representing higher nest-defense intensity in the year where gulls had larger number of adjacent neighboring nests (5.23 nests), but this trend was not observed in the year where they had smaller number of the neighboring nests (3.73 nests). Thus, in addition to testosterone levels, behavior of neighbors also influences the nest-defense intensity.
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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 | 0.000 |
| 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".