Do Arctic waders use adaptive wind drift?
Bibliographic record
Abstract
We analysed five data sets of flight directions of migrating arctic waders in relation to winds, recorded by tracking radar and optical range finder, in order to find out if these birds compensate for wind drift, or allow themselves to be drifted by winds. Our purpose was to investigate whether arctic waders use adaptive wind drift strategies or not. The data sets were collected in Siberia (two sets) and Canada during post‐breeding (autumn) migration, and in Mauritania and South Sweden during pre‐breeding (spring) migration. Both significant drift and compensation effects were found in three of the data sets, Canada, Mauritania and South Sweden. Almost no compensation was found in birds departing in easterly directions from the Siberian tundra (complete drift), while no drift effect was found in birds departing in westerly directions (complete compensation). There were indications that at least some populations of waders may use an adaptive drift strategy consisting of drift at high altitude and/or in high wind speed combined with compensation at low altitude and/or in lower wind speeds, but support for this idea was rather weak and not consistent. Our results were instead more in accordance with the adaptive drift theory that predicts initial drift during the migratory journey, followed by compensation during later stages as the birds are approaching their destinations. Such a strategy implies that arctic waders, at least adult birds, have the capacity of true navigation. A comparison with earlier studies of migrating arctic waders from different parts of the world show that all results so far may be interpreted in accordance with this general adaptive drift strategy. An element of non‐adaptive drift can, however, not be completely ruled out.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 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".