Recent Hybridization between a Polar Bear and Grizzly Bears in the Canadian Arctic
Bibliographic record
Abstract
Grizzly bears have recently become more common on the Arctic Islands in the Inuvialuit Settlement Region, concurrently with a period of environmental change. Over the last decade, grizzly bear – polar bear hybrids have been confirmed within this region, triggering extensive discussion and speculation regarding the impact of hybridization on the parent species. Through harvests, sightings, and captures, we document an increase in the presence of grizzly bears and combine field observations of hybrids with genetic analysis and parentage analysis to identify four first-generation (F1) hybrids and four offspring of F1 hybrids and grizzly bears (backcross-to-grizzly-bear individuals). We trace these eight hybrid individuals to a single female polar bear who mated with two grizzly bears. We sampled one of her mates on the sea ice in the High Arctic and deduced the genotype of the other from his five offspring. The two male grizzly bears are sires of both the F1 generation and the backcross-to-grizzly-bear generation. So what initially appeared to be a sudden spate of hybridization in the western Canadian Arctic originated with the unusual mating between three non-hybrid parents. The breakdown of species barriers may start with atypical mating preferences of select individuals; however, the story we present can be traced to a single female polar bear who, along with three of her known F1 offspring, has been killed.
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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 | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".