Data from: "Polar bear (Ursus maritimus) transcriptome assembly and SNP discovery" in Genomic Resources Notes accepted 1 August 2013-30 September 2013
Bibliographic record
Abstract
Polar bears (Ursus maritimus) in the Western Hudson Bay subpopulation have been declining in size and body condition for decades, as climate change causes earlier sea ice breakup, reduced hunting time on the ice, and an increasingly long fasting season. As Western Hudson Bay females have decreased in size, rates of litter production and average litter size have also decreased, while cub mortality and average time to independence have increased. Although these changes have potential evolutionary consequences, little is yet known about the adaptive genetic variation in body size or fat accumulation that would have to underlie any such change. In this study, we used high-throughput Illumina sequencing to develop SNPs from pooled blood and fat transcriptomes, using samples from five adult female polar bears and five (unrelated) dependent cubs. In total, we generated 371,258 transcripts of which 36,755 were deemed to be “full length” (i.e., covered more than 90% of their best BLAST hit), and we identified 63,020 SNPs. Since this study was conducted, we have used a subset of these SNPs to develop an Illumina BeadArray for quantitative genetics research in Western Hudson Bay.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.110 | 0.085 |
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".