Observations of Annual Walrus (<i>Odobenus rosmarus divergens</i>) Migrations in the Nearshore Waters of the Chukotka Peninsula from 1990 to 2012
Bibliographic record
Abstract
After the end of the commercial harvest, research regarding the Pacific walrus (Odobenus rosmarus divergens) in Russia was reduced and focused on the observation of animals at land-based haul-outs. This paper presents the long-term observations of the distribution, relative abundance, and direction of seasonal movements of walruses in the offshore waters of the Chukotka Peninsula, based on data obtained in 1990 – 2012. Observations of Pacific walruses and other marine mammals were conducted mainly from April through November, but some were conducted all year round. In some years up to 30 Native Chukotkan observers were employed at this task. Some watched from observation posts in Native villages onshore, and others from motorboats during hunting trips. These observations have shown that walruses are rare in January and February in the nearshore waters of the Chukotka Peninsula. Their numbers begin to increase in March. The northward movement of walruses begins in April, and walruses migrate from the Bering Sea to the Chukchi Sea throughout the summer months and early autumn. Based on observations from posts located directly in front of the southern Bering Strait, I conclude that 106 – 1055 walruses pass through the Bering Strait from July to September, to the northwest and north. At the haul-outs in the Gulf of Anadyr, the relative number of walruses remains stable during the summer (up to 11 000 individuals at all haul-outs in total based on observers’ estimates) and decreases only with the appearance of ice in October – November.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".