Estimating the abundance of a polar bear subpopulation at their southern global extent
Bibliographic record
Abstract
Climate warming is causing global biodiversity loss, with impacts to ecosystem function. Warming in the Arctic outpaces global averages, and projected declines in Arctic sea ice have led to predictions of local extirpations for ice-associated species. Polar bears ( Ursus maritimus Phipps, 1774) exemplify these challenges as they rely on sea ice for much of their life cycle. Further, polar bears are harvested throughout much of their range, increasing the importance of robust population monitoring in the face of climate warming. We conducted an aerial survey in summer 2021 to estimate abundance of the Southern Hudson Bay polar bear subpopulation. We estimated 1119 polar bears (95% CI = 860–1454) within the boundaries of the subpopulation, which suggested a 29% increase from the previous aerial survey in 2016. This increase was likely driven by a combination of interannual variation in the on-land distribution of bears in the Southern Hudson Bay and adjacent Western Hudson Bay polar bear subpopulations as well as reduced harvest and improved survival. Evidence from concurrent research suggests that variation in on-land distribution is the most likely driver. These results exemplify the challenges of monitoring and, particularly, managing harvest of sensitive species under the rapid environmental change caused by climate warming. Further research is needed and critical for effective harvest management of this subpopulation.
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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 | 0.001 | 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 | 0.000 | 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".