SOARING AND GLIDING FLIGHT OF MIGRATING BROAD-WINGED HAWKS: BEHAVIOR IN THE NEARCTIC AND NEOTROPICS COMPARED
Bibliographic record
Abstract
We compared migrating behavior of Broad-winged Hawks (Buteo platypterus) at two sites along their migration corridor: Hawk Mountain Sanctuary in eastern Pennsylvania and the Kéköldi Indigenous Reserve in Limón, Costa Rica. We counted the number of times focal birds intermittently flapped their wings and recorded the general flight type (straight-line soaring and gliding on flexed wings versus circle-soaring on fully extended wings). We used a logistic model to evaluate which conditions were good for soaring by calculating the probability of occurrence or absence of wing flaps. Considering that even intermittent flapping is energetically more expensive than pure soaring and gliding flight, we restricted a second analysis to birds that flapped during observations, and used the number of flaps to evaluate factors influencing the cost of migration. Both the occurrence and extent of flapping were greater in Pennsylvania than in Costa Rica, and during periods of straight-line soaring and gliding flight compared with circle-soaring. At both sites, flapping was more likely during rainy weather and early and late in the day compared with the middle of the day. Birds in Costa Rica flew in larger flocks than those in Pennsylvania, and birds flying in large flocks flapped less than those flying alone or in smaller flocks. In Pennsylvania, but not in Costa Rica, the number of flaps was higher when skies were overcast than when skies were clear or partly cloudy. In Costa Rica, but not in Pennsylvania, flapping decreased as temperature increased. Our results indicate that birds migrating in large flocks do so more efficiently than those flying alone and in smaller flocks, and that overall, soaring conditions are better in Costa Rica than in Pennsylvania. We discuss how differences in instantaneous migration costs at the two sites may shift the species' migration strategy from one of time minimization in Pennsylvania to one of energy minimization in Costa Rica.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".