Weather Effects on Autumn Nocturnal Migration of Passerines on Opposite Shores of the St. Lawrence Estuary
Bibliographic record
Abstract
We modeled migration intensity as a function of weather, using nightly migration measurements from Doppler surveillance weather radar during autumn migration on the north (Côte-Nord) and south (Gaspésie) shores of the St. Lawrence estuary, Québec, Canada. The radar had negative elevation angles, an uncommon characteristic among weather radars, which allowed simultaneous low-altitude monitoring of bird migration on each side of the estuary. Precipitation and wind both had strong effects on the intensity of migration. Very few birds migrated when >40% of the area had precipitation, especially when winds were strong. Light winds were associated with the strongest migration intensity, regardless of wind direction; in stronger winds, migration was likely only when winds were predominantly from the north. Days immediately after adverse weather events, which are assumed to lead to an accumulation of migrants, were associated with an increase in the intensity of migration in Côte-Nord, but not in Gaspésie. Time since the passage of a cold front had no effect in either region. Bird flight direction and behavior in relation to wind differed on each side of the estuary. On Côte-Nord, birds tended to migrate in a southwesterly direction along the St. Lawrence north coast, in a direction relatively unaffected by wind direction; they compensated or overcompensated for wind drift by following the coast. By contrast, birds in Gaspésie tended to fly in a more southerly direction. They migrated partially or almost fully downwind with only limited compensation, their flight direction often changing with wind direction.
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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".