Foraging effort of Surf Scoters (Melanitta perspicillata) wintering in a spatially and temporally variable prey landscape
Bibliographic record
Abstract
We monitored foraging effort of radio-tagged Surf Scoters (Melanitta perspicillata (L., 1758)) in three different habitats: (1) shellfish farm structures with high densities of mussels and strong seasonal prey depletion, (2) soft-bottom clam beds with more stable but less available prey, and (3) rocky intertidal beds with moderate mussel densities and depletion rates. We predicted that foraging effort would vary uniquely by habitat, with effort increasing more where depletion was strongest. However, variation in both hourly and daily foraging efforts was best explained by date only. Effort per hour was lowest in early December (presumably owing to very high prey abundance), increased until mid-February as prey declined, and then decreased again in March (probably owing to increased daylight time for foraging). Foraging effort estimated over a full day increased steadily from December to March as prey were depleted. Temporal patterns of effort did not vary by habitat after accounting for seasonal effects. Instead of increasing foraging effort in habitats with strong depletion, Surf Scoters redistributed to habitats with lower degrees of prey reduction as the season progressed. We suggest that Surf Scoters respond to variation in prey by adjusting both foraging effort and habitat selection as the prey landscape changes.
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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".