THE THERMAL REGIME OF EGGS DURING LAYING AND INCUBATION IN GREATER SNOW GEESE
Bibliographic record
Abstract
Dummy eggs were added to naturally incubated clutches of Greater Snow Geese (Chen caerulescens atlantica), an arctic-nesting species, in order to evaluate egg temperature during laying and incubation, and factors influencing egg cooling rate during female recesses. As laying progressed, both nest attentiveness by females and egg temperature progressively increased. Although the time spent at nest after laying the penultimate egg was relatively high (69% vs. 91% during incubation), mean egg temperature was still 5.7°C lower than during the early incubation period. This suggests that little embryonic development began before clutch completion. Thereafter, egg temperature averaged 37.1 ± 0.1°C during periods where females were present, a value that decreased only slightly when incubation recesses are included (36.8°C). This is a high temperature in comparison to other arctic-nesting geese. A modest increase (1.7°C) in mean egg temperature was observed as incubation progressed, but egg temperature was not influenced by clutch size or by the laying date of the first egg. During recesses lasting 24.7 ± 1.3 min on average, egg temperature dropped by 2.8 ± 0.3°C, at an instantaneous rate of 0.23 ± 0.02°C hr−1 °C−1. Cooling rates increased under windy conditions and decreased with high solar radiation, but were little affected by air temperature.
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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) | 0.000 | 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".