Bibliographic record
Abstract
Whether it is for the Olympics, pearl diving or climbing Mount Everest, humans have to train intensively to prepare their bodies for extreme challenges. However, it is not entirely clear whether other members of the animal kingdom also go through bouts of training prior to extreme events. Bar-headed geese (Anser indicus) have a reputation for performing one of the most extreme feats at altitude. Migrating across the Himalayas in as little as 7 h, the birds have to sustain one of the most metabolically costly forms of locomotion – flight – in high-altitude conditions, where oxygen is scarce. What is more, these birds fly straight from sea level to altitudes of more than 4500 m in a matter of hours, unlike humans, which need days to gradually habituate to high-altitude conditions. Working with the geese as part of a multinational collaborative project, Lucy Hawkes from the Centre for Ecology and Conservation at the University of Exeter, UK, and her colleagues set out to determine whether these birds ‘train’ for their annual high-altitude migration.Travelling to Terkhiin Tsagaan lake in Mongolia – where the bar-headed geese moult and are unable to fly, during a 2–3 week period prior to migration – Hawkes and colleagues captured small groups of the geese by herding them with inflatable kayaks into shoreline nets. The team then surgically implanted data loggers in the abdomens of the birds to record their heart rate, acceleration and internal temperature and pressure. They then released the animals and recaptured them a year later, when the geese returned to the lake after their migration in order to remove the loggers and download the data.To determine whether the birds trained for their migration, the authors looked at the activity that the animals undertook and the variation in their heart rate during the pre-migratory period. To estimate whether the geese exercised more prior to migration, the authors measured the acceleration produced by the animals as they moved around, which can clearly distinguish flapping from walking and resting. They found that the total amount of activity did not increase prior to migration, nor did the frequency of strenuous flapping on the ground – short, intense ground-based social displays which may also double as strength-training. If the geese were becoming physically fitter, the team also reckoned that their minimum, overnight heart rate would get lower, and their maximum heart rate during flight should get higher, but they found no evidence of either. Compiling the observations, Hawkes and her co-authors could not find evidence that bar-headed geese engage in any type of flight training activity prior to migration.Hawkes and her colleagues also suggest that perhaps bar-headed geese use the rest periods during migration to allow their muscles to recover and rebuild, which may increase their fitness over the course of the migration and could explain the lack of training prior to their departure. In addition, the authors suggest that the cardiac and respiratory systems of the bar-headed geese play an important role in ensuring their impressive performances at altitude, as they are adapted to take up as much oxygen as possible from the environment and quickly deliver it to the organs where it is needed. It appears that a combination of adaptations allows the bar-headed geese to fly high, no training required.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.260 | 0.089 |
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".