Aerial surveys suggest long‐term stability in the seasonally ice‐free Foxe Basin (Nunavut) polar bear population
Bibliographic record
Abstract
Abstract Significant information gaps exist regarding the status of polar bears, especially with respect to the impacts of climate change, across large portions of the Arctic. To obtain an updated abundance estimate for the Foxe Basin population, we conducted comprehensive aerial surveys during the 2009 and 2010 ice‐free seasons, when bears are confined to land. We sampled with mark‐recapture distance sampling protocols on inland and coastal transects and surveyed small islands and remnant ice floes. We observed 816 and 1,003 bears in 2009 and 2010, respectively. Although detection functions differed substantially between years, estimates were consistent between analytical methods and years. Averaging four estimates (two from each year) yielded 2,585 (2,096–3,189) bears, which is similar to an estimate from the 1990s. This result, along with robust cub production, suggests a stable and healthy population despite deteriorating sea ice conditions. Collectively, this and other recent on‐land surveys provide a framework for implementing aerial surveys elsewhere. Although aerial surveys do not yield estimates of vital rates or population growth, they enable more rapid and frequent monitoring than mark‐recapture. Integrating them in long‐term monitoring programs will require consideration of ancillary data to infer status and facilitate setting harvest levels.
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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 | 0.000 |
| 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".