Flight response of slope‐soaring birds to seasonal variation in thermal generation
Bibliographic record
Abstract
Summary Animals respond to a variety of environmental cues, including weather conditions, when migrating. Understanding the relationship between weather and migration behaviour is vital to assessing time‐ and energy limitations of soaring birds. Different soaring modes have different efficiencies, are dependent upon different types of subsidized lift and are weather dependent. We collected GPS locations from 47 known‐age golden eagles that moved along 83 migration tracks. We paired each location with weather to determine meteorological correlates of migration during spring and fall as birds crossed three distinct ecoregions in north‐east North America. Golden eagle migration was associated with weather conditions that promoted thermal development, regardless of season, ecoregion or age. Eagle migration showed age‐ and season‐specific responses to weather conditions that promoted orographic lift. In spring, adult eagles migrated earlier, over fewer days, and under more variable weather conditions than did pre‐adults, suggesting that adults were time limited and pre‐adults made choices to conserve energy. In fall, we found no difference in the time span of migration or when each age class migrates; however, we saw evidence that pre‐adults were less efficient migrants than adults. The decision by soaring birds to migrate when thermals developed allowed individuals to manage trade‐offs between migratory speed and migratory efficiency. When time was limited (i.e. spring movement of adults speeding towards nesting territories), use of whatever lift was available decreased the time span of migration. When migration was not time limited (e.g. spring movements by pre‐adults, all movements in fall), eagles avoided suboptimal flight conditions by pausing migration, thus increasing the time span of migration while reducing energetic costs.
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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".