The effect of postural adjustment on the thermal environment of greater snow goose goslings
Bibliographic record
Abstract
This study examines how changing from a standing to a sitting posture influences the thermal environment of greater snow goose goslings (Chen caerulescens atlantica). This was investigated by estimating the standard operative temperature of four heated taxidermic mounts (3, 10, 20, and 30 d old) exposed to various wind velocities (0-5 m/s) and ground (16-23°C) and air (0-15°C) temperatures, in three orientations (head, flank, or tail toward the wind) and two postures (sitting and standing). Changes in posture influenced both conductive and convective heat exchanges. At low wind speeds, sitting on the sand reduced the standard operative temperature of goslings, while at high wind speeds sitting enhanced this temperature index. We calculated that a net thermal gain would be obtained by sitting on cold sand at air temperatures of 5, 10, and 15°C when the wind speed exceeded 3 m/s for most orientations toward the incoming wind. However, this critical wind speed would be 23% lower following a 7°C increase in ground temperature. Our study suggests that postural changes can have important consequences on goslings' thermal environment. It also stresses the importance of considering the synergistic impact of conductive and convective heat transfer processes, when studying the impact of postural changes on thermal environments.
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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".