Subpopulation delineation of Canadian polar bears (Ursus maritimus) in the eastern Beaufort Sea
Bibliographic record
Abstract
Wildlife management often delineates a species into units to improve monitoring, population estimation, and status assessment. Polar bears (Ursus maritimus) are delineated in 20 subpopulations based on an International Union for the Conservation of Nature definition. This definition requires subpopulations to be geographically distinct groups of individuals with low demographic or genetic exchange. I examined whether the Southern Beaufort Sea and Northern Beaufort Sea, were spatially separated using polar bear telemetry data collected between 2007-2014. To assess possible methods of subpopulation delineation, I grouped 75 adult and sub-adult bears into spatial groups using three classification methods: an observed space-use, a capture location, and an agglomerative hierarchical clustering. I then estimated the overlap between spatial groups during the harvest (February – June) and non-harvest (July – January) periods for each classification method. My results found that polar bears within the eastern Beaufort Sea are not geographically separated based on any of the classification methods, and that 61 bears crossed a subpopulation boundary. This assessment suggests that the entire eastern Beaufort Sea region represents one subpopulation. Any boundary in the eastern Beaufort Sea that separates polar bears into groups would best be considered as delineating wildlife management units rather than unique subpopulations based on biological separation.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".