Re-assessing abundance of Southern Hudson Bay polar bears by aerial survey: effects of climate change at the southern edge of the range
Bibliographic record
Abstract
The Southern Hudson Bay polar bear (Ursus maritimus Phipps, 1774) subpopulation is considered stable, but conflicting evidence lends uncertainty to that designation. Capture–recapture studies conducted in 1984–1986 and 2003–2005 and an aerial survey conducted in 2011/2012 suggested that abundance was likely unchanged since the mid-1980s. However, body condition and body size declined since then, and duration of sea ice decreased by about 30 days. Due to the conflicting information on subpopulation status and ongoing changes in sea ice, we conducted another aerial survey in 2016 to determine whether abundance had changed. We collected data via mark–recapture distance sampling and double-observer protocols. Results suggest that abundance declined 17% from 943 bears (95% CI: 658–1350) in 2011/2012 to 780 (95% CI: 590–1029) in 2016. The proportion of yearlings declined from 12% of the population in 2011 to 5% in 2016, whereas the proportion of cubs remained similar (16% in 2011 vs. 19% in 2016) suggesting low survival of the 2015 cohort. In a warming Arctic, duration of sea ice is predicted to continue to decline in Hudson Bay affecting all ice-dependent wildlife; therefore, further monitoring of this subpopulation is warranted. We recommend a conservative approach to harvest management and repeating the aerial survey in 2021.
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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 | 0.003 | 0.003 |
| 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 | 0.001 |
| 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".