Heart rate and metabolic rate of bar‐headed geese flying in hypoxia
Bibliographic record
Abstract
Bar‐headed geese accomplish the extraordinary feat of migrating over the Himalayas, where oxygen (O 2 ) levels are only ½ ‐ 1/3 those at sea‐level. Although physiological responses and adaptations relevant to this species’ success at high altitude have been previously documented, only one study has measured physiological variables in this bird during flight, and only under conditions of normoxia. In order to assess the roles of the cardiovascular and respiratory systems in maintaining oxygen delivery during flight in hypoxia, we trained bar‐headed geese to fly in a wind tunnel while wearing our physiological data‐logger and a mask system. We were successful in measuring heart rate and metabolic rate in flying geese under conditions of normoxia and hypoxia (10.5% O 2 and 7% O 2 , equivalent to altitudes of ~5,500 and ~8,500 meters respectively). Surprisingly, bar‐headed geese exhibited a remarkably wide range of heart rates and metabolic rates while flying at their preferred flight speed, even on an individual bird basis. Based on preliminary data, mean heart rate during flight changed very little with increasing levels of hypoxia, though mean metabolic rate was reduced. This suggests that the birds settled into more efficient flight patterns in the more challenging hypoxic environment. Bar‐headed geese sustained the same flight durations at equivalent flight speeds under conditions of normoxia and severe hypoxia.
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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".