Do Bar-Headed Geese Train for High Altitude Flights?
Bibliographic record
Abstract
SYNOPSIS: Exercise at high altitude is extremely challenging, largely due to hypobaric hypoxia (low oxygen levels brought about by low air pressure). In humans, the maximal rate of oxygen consumption decreases with increasing altitude, supporting progressively poorer performance. Bar-headed geese (Anser indicus) are renowned high altitude migrants and, although they appear to minimize altitude during migration where possible, they must fly over the Tibetan Plateau (mean altitude 4800 m) for much of their annual migration. This requires considerable cardiovascular effort, but no study has assessed the extent to which bar-headed geese may train prior to migration for long distances, or for high altitudes. Using implanted loggers that recorded heart rate, acceleration, pressure, and temperature, we found no evidence of training for migration in bar-headed geese. Geese showed no significant change in summed activity per day or maximal activity per day. There was also no significant change in maximum heart rate per day or minimum resting heart rate, which may be evidence of an increase in cardiac stroke volume if all other variables were to remain the same. We discuss the strategies used by bar-headed geese in the context of training undertaken by human mountaineers when preparing for high altitude, noting the differences between their respective cardiovascular physiology.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".