Development of an 8K SNP chip to assess adaptive diversity and hybridization in polar bears
Bibliographic record
Abstract
Abstract The polar bear (Ursus maritimus) is a species particularly vulnerable to the effects of climate change. As the climate warms, polar bears will be forced to move to more suitable habitats which are likely to shrink, adapt to the new conditions, or decline in population size. However, the genomic diversity within and among all 19 subpopulations of polar bears, and therefore their adaptive potential, is currently unknown. In addition, warmer climates are likely to result in more frequent contact between polar bears and grizzly bears (U. arctos), with which they can hybridize. Here we describe the development, quality control, and application of the Ursus maritimus V2 SNP chip. This 8 K SNP chip contains loci explicitly selected to assess both RAD-derived and transcriptome-derived loci, as well as SNPs to detect hybridization between species. A total of 7,239 loci (90.3% of those printed) were successfully genotyped, with over 99% genotype concordance for individuals typed in duplicate on this chip, and between individuals typed here and on the Ursus maritimus V1 SNP chip. Using simulations, we demonstrate that the markers have high accuracy and efficiency to detect hybridization and backcrosses between polar bears and grizzly bears. However, empirical analysis of 371 polar bears, 440 grizzly bears, and 8 known hybrids found no novel instances of recent hybridization. The Ursus maritimus V2 SNP chip provides a powerful tool for monitoring the adaptive potential of this species along with assessing population structure, quantitative genomics, and hybridization in polar bears.
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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 | 0.000 |
| 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) | 0.002 | 0.001 |
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".