The paradox of extreme high-altitude migration in bar-headed geese<i>Anser indicus</i>
Bibliographic record
Abstract
Bar-headed geese are renowned for migratory flights at extremely high altitudes over the world's tallest mountains, the Himalayas, where partial pressure of oxygen is dramatically reduced while flight costs, in terms of rate of oxygen consumption, are greatly increased. Such a mismatch is paradoxical, and it is not clear why geese might fly higher than is absolutely necessary. In addition, direct empirical measurements of high-altitude flight are lacking. We test whether migrating bar-headed geese actually minimize flight altitude and make use of favourable winds to reduce flight costs. By tracking 91 geese, we show that these birds typically travel through the valleys of the Himalayas and not over the summits. We report maximum flight altitudes of 7290 m and 6540 m for southbound and northbound geese, respectively, but with 95 per cent of locations received from less than 5489 m. Geese travelled along a route that was 112 km longer than the great circle (shortest distance) route, with transit ground speeds suggesting that they rarely profited from tailwinds. Bar-headed geese from these eastern populations generally travel only as high as the terrain beneath them dictates and rarely in profitable winds. Nevertheless, their migration represents an enormous challenge in conditions where humans and other mammals are only able to operate at levels well below their sea-level maxima.
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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 | 0.001 |
| Scholarly communication | 0.001 | 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".