The effect of depth on the diving behaviour of common eiders
Bibliographic record
Abstract
Diving in endotherms is largely constrained by the depth of the foraging area, as they withstand long periods without breathing. Foraging theory predicts that all phases of a dive cycle, including travel, bottom, and surface durations, are positively correlated with depth. With continued increase in depth, bottom duration should level off and then decline. We tested these qualitative predictions with common eiders (Somateria mollissima (L., 1758)), a sea duck foraging routinely on the bottom. Using data loggers implanted in the body cavity of four females for 1 year, we showed that average diving depth over the summer and winter was distinct for each female. When averaged for each female, the data were qualitatively similar to theoretical predictions since travel, bottom, and surface durations all increased with depth. However, within individual females, the data only supported the theory partially because some relationships between depth and phases of a dive cycle were not significant. In particular, bottom duration failed to increase with depth for two individual females during the summer. In addition, significant relationships differed substantially among individual females and even within individual females when summer and winter seasons were compared. We suggest that the large variation observed in the diving behaviour of female eiders reflects individual female diving capability, which is probably related to their physiological states.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".