A demographic survey of the Davis Strait polar bear subpopulation using physical and genetic capture‐recapture‐recovery sampling
Bibliographic record
Abstract
Abstract Conducting assessments to understand the effects of changing environmental conditions on polar bear (Ursus maritimus) demography has become increasingly important to inform management and conservation. Here, we combined physical (2005–2007) and genetic (2017–2018) mark‐recapture with harvest recovery data (2005–2018) to estimate demographic rates of the Davis Strait polar bear subpopulation and examine the possible effects of climate, dynamic ice habitat, and prey resources on survival. Large sample sizes (e.g., 2,513 marked animals) allowed us to estimate temporal variation in annual survival rates using multistate mark‐recapture‐recovery models. We did not detect statistically significant effects of climate, ice habitat, and prey during the 13‐year study. Estimated total abundance in 2006 was 2,190, credible interval (CRI) [1,954, 2,454] and 1,944, CRI [1,593, 2,366] in 2018. Geometric mean population growth rate (0.99, 95% CRI [0.97, 1.01]) indicated the subpopulation may have declined slightly between 2006 and 2018. However, we did not detect a declining trend in survival or substantial change in reproductive metrics over this period. Given forecasts of major environmental change we emphasize the need to review monitoring programs for 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".