Coping with darkness and low temperatures: foraging strategies in Steller's eiders, <i>Polysticta stelleri</i>, wintering at high latitudes
Bibliographic record
Abstract
To examine how Steller's eiders, Polysticta stelleri, wintering at 70°N cope with adverse winter conditions in terms of darkness and low temperatures, we studied their feeding behaviour during four periods between late autumn and early spring. Steller's eiders were most likely to feed during daylight and twilight, but they also fed during darkness. The incidence of feeding was highest at low tide, and there was a significant interaction between tidal cycle and winter period. Hence, the birds fed more intensively at low tide in midwinter (January) than during the other periods. Air temperatures were between 8 and 10°C lower in midwinter than during the other periods, and during this period the eiders also fed more by means of nondiving techniques (up-ending, surface feeding). The total estimated feeding time was highest in late autumn and midwinter (5.9 and 6.3 h were spent actively feeding, respectively) and lower in late winter and spring (5.1 and 4.6 h, respectively). Thus, as energy requirements increased as a result of low temperatures, Steller's eiders increased their feeding effort, but also reduced feeding costs by reducing diving depth. The results of this study suggest that the Steller's eider is behaviourally well adapted to survive winter at high latitudes at relatively low stress.
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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 | 0.000 | 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".