Breeding Partners Have Dissimilar Foraging Strategies in a Long‐Lived Arctic Seabird
Bibliographic record
Abstract
ABSTRACT For long‐lived species with biparental care, coordination and compatibility in the foraging behavior of breeding mates may be crucial to successfully raise offspring. While high foraging success is clearly important to reproductive success, it might be equally important that the mate has a complementary foraging strategy. We test whether breeding partners have similar or dissimilar foraging strategies in a species where both partners share breeding responsibilities and exhibit high mate fidelity (thick‐billed murre; Uria lomvia). To examine whether thick‐billed murres showed complementary in foraging strategies, we attached GPS accelerometers to both partners within 40 thick‐billed murre chick‐rearing pairs. Individuals within a breeding pair were dissimilar in their foraging trip distance and in their number of dives during foraging trips compared to randomized pairs. Breeding partners were also more similar in wing length than randomized pairs. This result could be related to individual quality as individuals select similar sized partners or select sites that lead to similar sized partners. We conclude that foraging strategy diversity could be maintained in this population either because individuals prefer partners with foraging strategies complementary to their own, or because partners diverge in foraging strategies over multiple breeding season together.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".