Bounding the Southern Hudson Bay polar bear subpopulation
Bibliographic record
Abstract
Polar bears (Ursus maritimus) are managed by the 5 nations where they occur (Canada, Greenland/Denmark, Norway, Russia, United States) using discrete subpopulations. In Canada, polar bears are harvested throughout their range, and several subpopulations are managed by more than one jurisdiction; therefore, recent management focused on ensuring sustainable polar bear harvests. Consequently, the subpopulation from which harvested bears are removed and the geographic boundaries of that subpopulation must be correctly identified. However, boundaries of the Southern Hudson Bay (SH) subpopulation have not been verified using satellite radio-telemetry data. Sea ice duration has already declined in Hudson Bay and James Bay, and both the duration and distribution of sea ice are predicted to decline greatly in the next century; therefore, it is important to document current habitat use patterns to assess the potential impacts of climate change. We used a probabilistic approach to describe the utilization distribution for the SH subpopulation based on data from 1997–2003 from 26 adult female bears fitted with satellite collars and assessed whether the currently accepted boundaries represent the population utilization distribution. We conclude that the SH boundaries do reflect the current spatial distribution of adult female bears in this subpopulation. Our analysis provides a benchmark to compare to the future distribution and habitat use of this subpopulation in response to effects of climate change and identifies future research needs to investigate polar bear distribution in James Bay and in the area near the boundary between the SH and Western Hudson Bay management zones.
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 | 0.000 | 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 | 0.001 |
| 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".