Northward migration, molting locations, and winter residency of California breeding pigeon guillemots Cepphus columba
Bibliographic record
Abstract
Pigeon guillemots Cepphus columba are ubiquitous along the coasts of the eastern North Pacific, yet little is known about their winter migration patterns, habitat needs, and potential threats faced during the non-breeding period. We used 3 seasons of year-long light level data from tagged individuals to estimate the migration timing and winter residency of pigeon guillemots breeding on Southeast Farallon Island in California (USA). Light level data were combined with a movement model to estimate positions of tagged animals, revealing that individuals from this population undertook a coordinated coastal migration north in the fall, stopping at sites near Haida Gwaii in British Columbia (Canada), presumably during a flightless prebasic molt, before continuing north to stationary overwintering sites in coastal British Columbia and Southeast Alaska. Birds then made an uninterrupted migration south in the spring, returning to waters around Southeast Farallon in late March and early April. Wet/dry data indicated nocturnal resting on land during the breeding season and likely on the water throughout the non-breeding months. This is the first study to confirm the migratory patterns of pigeon guillemots from California, and highlights the importance of the waters of British Columbia and Southeast Alaska for the studied population and possibly other major populations of this species.
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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".