TIMING AND DISTANCE OF KING EIDER MIGRATION AND WINTER MOVEMENTS
Bibliographic record
Abstract
Understanding the patterns, extent, and phenology of migration is important for estimating potential influences of habitat or climate changes on populations of migratory birds. We used satellite telemetry of >100 individual King Eiders (Somateria spectabilis) tagged in northwestern North America in 2002–2006 to describe the timing and extent of their migration and winter movements in the Bering Sea. We found high variability in timing of migration events and distances flown. Arrival on breeding grounds and onset of molt migration were the least variable events in duration. Fall migration was extremely variable, ranging from less than a week to several months. More than a third of King Eiders did not migrate after wing molt and wintered on or near wing-molting areas. We found diffuse migratory connectivity between breeding and wintering areas, and low intrayear fidelity to 25 km radius wintering sites. More than half of the King Eiders used several wintering sites in a given year, and their winter ranges were considerably larger than those of other sea duck species. We identified three distinct wintering regions in the Bering Sea that were several hundred km apart, among which no movements occurred from late December until April. The onset of spring migration was earlier for birds wintering farther south, but arrival time on breeding grounds was not correlated with wintering latitude. We conclude that high phenotypic plasticity in migratory traits may render King Eiders more likely to respond to environmental shifts than sea duck species that show stronger migratory connectivity.
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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 | 0.000 |
| 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".