Demographic assessment using physical and genetic sampling finds stable polar bear subpopulation in Gulf of Boothia, Canada
Bibliographic record
Abstract
Abstract Knowledge of long‐term demographic trends is important for managing large carnivore populations under changing environmental conditions, management objectives, and human values. From 2015 to 2017, we biopsy‐sampled polar bears ( Ursus maritimus ) in the Gulf of Boothia (GB) subpopulation to genetically identify individuals. This less‐invasive sampling method was more compatible with stakeholder values than chemical immobilization. We analyzed the biopsy data together with live‐capture study data (1998–2000), opportunistically collected live‐capture data (1976–1997), and harvest recovery data (1976–2017). From 2015 to 2017, the mean model‐averaged abundance estimate was 1,525 bears ( SE = 294), similar to both the 1998–2000 estimate from the current analysis (1,610 ± 266) and previously published estimate (1,592 ± 361). Total survival from 2015–2017 varied by sex and age class, with higher estimates for adult females (0.95, 95% CI [0.81, 0.99]) than adult males (0.85, 95% CI [0.74, 0.92]). Mean number of yearlings per adult female was 0.36, 95% CI [0.26, 0.47], suggesting healthy reproduction. Body condition improved between 1998–2000 and 2015–2017. Our findings suggest the GB subpopulation is currently productive and stable. Forecasts of continued sea‐ice loss and environmental change due to climate warming emphasize the need for ongoing monitoring of this subpopulation.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.003 |
| 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 teacher head, 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".