Experimental evaluation of nest shelter effects on weight loss in incubating common eiders <i>Somateria mollissima</i>
Bibliographic record
Abstract
Evaluating consequences of habitat selection is an important step in understanding life history strategies and behavioural decisions of animals. found that incubating common eiders Somateria mollissima on exposed, treeless islands lost weight faster than females nesting on wooded islands and proposed that this difference was due to adverse incubation conditions at exposed nests. Therefore, we tested whether common eiders gained an advantage when nesting in sheltered habitats by placing artificial shelters over randomly‐selected females after the onset of incubation within an eider colony in arctic Canada. We predicted that sheltered females would be heavier on completion of incubation than control hens lacking shelters. Females nesting in artificial shelters experienced a more moderate thermal environment at both cold and warm temperature extremes. Eiders nesting in shelters were heavier than control females during mid incubation, consistent with habitat‐specific rates of weight loss reported by . Natural overhead cover was available at potential nests but few eiders used those sites. We suspect that microclimatic advantages offered by sheltered sites may be offset by costs of increased female vulnerability to predators. Further work is needed to test this hypothesis, and to determine mechanisms responsible for lower weight loss in eiders attending well concealed nests.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".