Individual and colony-specific wintering areas of Pacific northern fulmars (Fulmarus glacialis)
Bibliographic record
Abstract
Seabird mortality associated with longline fishing in the eastern Bering Sea occurs mainly from September to May, with northern fulmars ( Fulmarus glacialis ) comprising the majority (60%) of the bycatch. Along the west coast of North America, winter dieoffs of fulmars may be increasing in frequency and magnitude, the most severe on record being a wreck that peaked in October–November 2003. We deployed satellite transmitters on fulmars from the four main Alaska colonies and tracked individuals for up to 2 years. Fulmars from Hall Island (northern Bering Sea) moved to Russian coastal waters after breeding, while Pribilof Island fulmars (southeastern Bering Sea) remained relatively sedentary year-round. Birds from Chagulak Island (eastern Aleutians) preferred passes between the Aleutian Islands in winter or foraged widely over deep waters of the central Bering Sea and North Pacific. Fulmars from the Semidi Islands (western Gulf of Alaska) migrated directly to waters of the California Current. Individuals from St. George Island (Pribilofs) and Chagulak were consistent in the places that they visited in two successive winters. The Pribilof Islands population is most affected by winter longlining for groundfish, whereas the Semidi Islands colony sustains most of the natural mortality that occurs off Washington, Oregon, and California.
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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".