Habitat use and habitat selection by spotted seals (<i>Phoca largha</i>) in the Bering Sea
Bibliographic record
Abstract
Twelve spotted seals (Phoca largha) equipped with satellite-linked tags were tracked in the Bering Sea for 46-272 days during August-June 1991-1994. Alaskan seals were mostly near shore during August-October and 100-200 km offshore in January-June, and were broadly distributed in the region north of the 200-m isobath. Russian seals were located primarily near shore and within 100 km of the 200-m isobath during all months. During August-October, all seals were usually more than 200 km south of the sea-ice edge. In January-June, seals were mostly 0-200 km north of the sea-ice edge, often in areas with extensive ice coverage (7/10-9/10). We tested for habitat selection by determining how frequently a randomly moving seal would have been located in each habitat and comparing that with observed habitat use. Russian seals selected for nearshore and shallow-water areas in September-October and for near shore, within 25 km of the 200-m isobath, and the ice front during November-April. Alaskan seals selected for near shore areas in September-December; for offshore, shallow water, and the ice front in January-February; and for shallow water and pack ice in March-April. Biological processes associated with the highly productive "Green Belt" may have influenced the habitat use of Russian seals, but this did not appear to have been the case with Alaskan seals.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".