Factors affecting individual foraging behavior in a threatened seabird: Olrog’s Gull (<i>Larus atlanticus</i>) as a case study
Bibliographic record
Abstract
The analysis of feeding strategies in animals is one of the most important topics in foraging ecology. The individual’s foraging behavior depends on both the individual’s own actions and the behavior of other foragers. Here we analyse the effect of the sex and group size on the foraging behavior of immature Olrog’s Gull (Larus atlanticus Olrog, 1958), endemic to the Atlantic coast of southern South America and regionally listed a threatened species. Birds were captured, banded, sexed, and aged during the non-breeding season in Mar Chiquita coastal lagoon, Argentina. The foraging behavior was quantified by observations made on individuals of known identity, recording the size of foraging groups, as well as prey size and type. Foraging parameters estimated were foraging effort, capture rate, and foraging efficiency. Males spent more time in agonistic behavior and captured larger prey. With an increase in group size, the capture rate, the capture of small crabs by males, and the foraging effort were higher. The agonistic behaviors, size of consumed prey, and foraging effort were affected by individual identity. Our study pinpoints factors underlying variation in the foraging behavior of Olrog’s Gulls and illustrates the importance of modelling individual variation when analyzing foraging behavior.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".