Contribution of hybridization between polar bears and grizzly bears to polar bear extinction
Bibliographic record
Abstract
Abstract This review explores whether recent hybridization events between polar bears (Ursus maritimus) and grizzly bears (Ursus arctos horribilis) will eventually contribute to the extinction of the modern polar bear. In April 2006, genetic analysis of an odd‐looking bear killed in the Northwest Territories, Canada, revealed this specimen to be a polar bear–grizzly bear hybrid, and additional hybrid bears were harvested beginning in April 2010. These events have been sensationalized by some in the media, who have speculated that the modern polar bear may become extinct due to crossbreeding with grizzly bears. Although studies support the introgression of brown bear genes into the polar bear genome during the Pleistocene, no evidence supports the occurrence of a similar event today. Even if such an introgression event occurred, hybrids evolving rapidly enough to adapt to ongoing sea ice depletion is not scientifically plausible. The loss of genetic integrity and modified morphology due to inbreeding between polar bears and grizzly bears are not considered threats to polar bear survival. As many scientists have stressed for decades, the greatest threat to the survival of the modern polar bear is sea ice depletion due to climate change.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".