Tackling the Tibetan Plateau in a down suit: insights into thermoregulation by bar-headed geese during migration
Bibliographic record
Abstract
ABSTRACT Birds migrating through extreme environments can experience a range of challenges while meeting the demands of flight, including highly variable ambient temperatures, humidity and oxygen levels. However, there has been limited research into avian thermoregulation during migration in extreme environments. This study aimed to investigate the effect of flight performance and high altitude on body temperature (Tb) of free-flying bar-headed geese (Anser indicus), a species that completes a high-altitude trans-Himalayan migration through very cold, hypoxic environments. We measured abdominal Tb, along with altitude (via changes in barometric pressure), heart rate and body acceleration of bar-headed geese during their migration across the Tibetan Plateau. Bar-headed geese vary the circadian rhythm of Tb in response to migration, with peak daily Tb during daytime hours outside of migration but early in the morning or overnight during migration, reflecting changes in body acceleration. However, during flight, changes in Tb were not consistent with changes in flight performance (as measured by heart rate or rate of ascent) or altitude. Overall, our results suggest that bar-headed geese are able to thermoregulate during high-altitude migration, maintaining Tb within a relatively narrow range despite appreciable variation in flight intensity and environmental conditions.
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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".