The Polar Bear Management Agreement for the Southern Beaufort Sea : An Evaluation of the First Ten Years of a Unique Conservation Agreement
Bibliographic record
Abstract
Polar bears (Ursus maritimus) of the southern Beaufort Sea population, distributed from approximately Icy Cape, west of Point Barrow, Alaska, to Pearce Point, east of Paulatuk in Canada, are harvested by hunters from both countries. In Canada, quotas to control polar bear hunting have been in place, with periodic modifications, since 1968. In Alaska, passage of the United Sates Marine Mammal Protection Act (MMPA) of 1972 banned polar bear hunting unless done by Alaska Natives for subsistence hunt, leaving open the potential for an overharvest with no possible legal management response until the population was declared depleted. Recognizing that as a threat to the conservation of the shared polar bear population, the Inuvialuit Game Council from Canada and the North Slope Borough from Alaska negotiated and signed a user-to-user agreement, the Polar Bear Management Agreement for the Southern Beaufort Sea, in 1988. We reviewed the functioning of the agreement through its first 10 years and concluded that, overall, it has been successful because both the total harvest and the proportion of females in the harvest have been contained within sustainable limits. However, harvest monitoring needs to be improved in Alaska, and awareness of the need to prevent overharvest of females needs to be increased in both countries. This agreement is a useful model for other user-to-user conservation agreements.
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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 | 0.023 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".