Variability of Cetacean Distribution and Habitat Selection in the Alaskan Arctic, Autumn 1982-91
Bibliographic record
Abstract
Ten years (1982-91) of autumn sighting data from aerial surveys offshore northern Alaska were analyzed to investigate variability in cetacean distribution and habitat selection. Habitat selection indices were calculated for bowhead, white, and gray whales in heavy, moderate, and light ice conditions; and for high, moderate, and low transport (inflow) conditions at Bering Strait. Bowhead whales selected shallow inner-shelf waters during moderate and light ice, and deeper slope habitat in heavy ice conditions (chi², p < 0.05-0.001). White whales selected slope habitat (chi², p < 0.001), and gray whales selected coastal/shoal and shelf/trough habitat (chi², p < 0.025-0.001), in all ice conditions. In the Alaskan Beaufort Sea, bowheads selected shelf waters and white whales chose slope waters, without regard to transport conditions (chi², p < 0.01-0.001). In the northern Chukchi Sea, gray whales selected coastal/shoal habitat in high transport conditions (chi², p < 0.005), and shelf/trough habitat (chi², p < 0.001) during moderate and low transport conditions. Variability in distribution and habitat selection among these species is likely linked to prey availability at dissimilar trophic levels, although this hypothesis has yet to be rigorously tested.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".