Seasonal variation in energy expenditure is not related to activity level or water temperature in a large diving bird
Bibliographic record
Abstract
There is considerable interest in understanding how the energy budget of an endotherm is modulated from a physiological and ecological point of view. In this paper, we used daily (24 h) heart rate (f(H24)), as a proxy of daily energy expenditure (DEE) across seasons, to test the effect of locomotion activity and water temperature on the energy budget of a large diving bird. f(H24) was monitored continuously in common eiders (Somateria mollissima) during 7 months together with measures of time spent flying and time spent feeding. f(H24) varied substantially during the recording period, with numerous increases and decreases that occurred across seasons, although we did not find any relationship between f(H24) and the time spent active (feeding and flying). However, inactive heart rate (f(H,inactive)) decreased as locomotion activity increased, suggesting that common eiders were using some form of compensation when under a high work load. We were also unable to detect a negative relationship between water temperature and resting heart rate, a proxy of resting metabolic rate. This was unexpected, based on the assumption that high thermoregulation costs would be associated with cold waters. We showed instead that a high level of energy expenditure coincided with feather moult and warm waters, which suggests that the observed variable pattern of seasonal DEE was driven by these two factors. Nevertheless, our results indicate that compensation and possibly the timing of moult may be used as mechanisms to reduce seasonal variation in energy expenditure.
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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 | 0.001 | 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".