Rapid advancement of spring migration and en route adjustment of migration timing in response to weather during fall migration in Vaux’s Swifts (<i>Chaetura vauxi</i>)
Bibliographic record
Abstract
Climate change has generated earlier springs, later falls, and different weather patterns. These changes may prove challenging to migratory species if they are unable to adjust their migratory timing. We analyzed changes in migratory timing of Vaux’s Swifts (Chaetura vauxi (J.K. Townsend, 1839)) by examining first arrivals (date the first swift arrived) and peak roost occupancy (date the maximum number of swifts were observed) at migratory roosts in both spring and fall from the citizen science organization Vaux’s Happening. First arrivals and peak occupancy date in Vaux’s Swifts advanced over time from 2008 to 2017, and the timing of first arrivals advanced with an increase in local wind gust speeds. In contrast, fall migration timing did not change over time from 2008 to 2016, but higher temperatures were associated with later fall migration (both first arrival and peak roost occupancy) and higher local wind speeds were associated with earlier fall migration (peak roost occupancy only). Like many other migratory birds, Vaux’s Swifts may be tracking earlier spring phenology, and may also be altering their migratory timing in response to local weather conditions, especially during fall migration. Our results indicate that swifts may be able to adjust their migration to a changing climate, at least in the short term.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".