Adaptive aspects of intraclutch egg-size variation in the High Arctic barnacle goose (<i>Branta leucopsis</i>)
Bibliographic record
Abstract
The pattern of intraclutch egg-size variation in barnacle goose (Branta leucopsis) clutches and its adaptive implications was studied in Svalbard, Norway, from 1989 to 1998. Egg size was measured in relation to laying sequence, egg predation and hatching order were recorded to determine hatching success of eggs in different laying sequences, and the time when incubation started was examined. Egg size showed a rather consistent pattern, with a large second-laid egg and declining egg size for the remainder of the clutch. The first-laid egg was usually smaller than the second one, except in clutches with two and three eggs. Predation was highest for the first-laid egg, and last-laid eggs hatched last in most cases, although only one last-laid egg was abandoned. Four of six females started incubation before clutch completion. Both the "nutrient-allocation hypothesis" as well as the "early incubation start hypothesis" may contribute to explaining the expressed pattern of intraclutch egg-size variation. The fitness gains due to allocating fewer nutrients to eggs in unfavourable positions in the laying sequence may explain the small size of the first egg, whereas the multiple benefits of an early incubation start may have led to the decline in egg size later in the laying sequence as a mechanism to counteract hatching asynchrony.
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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".