Microsatellite DNA and mitochondrial DNA variation in polar bears (<i>Ursus maritimus</i>) from the Beaufort and Chukchi seas, Alaska
Bibliographic record
Abstract
Radiotelemetry data have shown that polar bears ( Ursus maritimus Phipps, 1774 ) occur in separate subpopulations in the Chukchi Sea and the southern Beaufort Sea. However, segregation is not absolute, and there is overlap of ranges of animals in each subpopulation. We used genetic variation at eight microsatellite DNA loci and mitochondrial DNA (mtDNA) to further assess the degree of spatial structure of polar bears from the Chukchi and southern Beaufort seas. Microsatellite allele frequencies and mtDNA haplotype frequencies of bears from the southern Beaufort and Chukchi seas did not differ significantly. Lack of differentiation at both maternally inherited mtDNA and bi-parentally inherited microsatellite loci suggests that gene flow between the two areas is mediated by both sexes. The genetic data indicate that polar bears in the southern Beaufort and Chukchi seas compose one interbreeding population. However, there is considerable fidelity to ranges in each area, particularly by adult females. The combined genetic and movement data suggest that polar bears could be managed as Beaufort Sea and Chukchi Sea subpopulations of a combined southern Beaufort Sea and Chukchi Sea population.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".