Flight responses by a migratory soaring raptor to changing meteorological conditions
Bibliographic record
Abstract
Soaring birds that undertake long-distance migration should develop strategies to minimize the energetic costs of endurance flight. This is relevant because condition upon completion of migration has direct consequences for fecundity, fitness and thus, demography. Therefore, strong evolutionary pressures are expected for energy minimization tactics linked to weather and topography. Importantly, the minute-by-minute mechanisms birds use to subsidize migration in variable weather are largely unknown, in large part because of the technological limitations in studying detailed long-distance bird flight. Here, we show golden eagle (Aquila chrysaetos) migratory response to changing meteorological conditions as monitored by high-resolution telemetry. In contrast to expectations, responses to meteorological variability were stereotyped across the 10 individuals studied. Eagles reacted to increased wind speed by using more orographic lift and less thermal lift. Concomitantly, as use of thermals decreased, variation in flight speed and altitude also decreased. These results demonstrate how soaring migrant birds can minimize energetic expenditures, they show the context for avian decisions and choices of specific instantaneous flight mechanisms and they have important implications for design of bird-friendly wind energy.
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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".