Development of a new SNP panel for bowhead whales (Balaena mysticetus)
Bibliographic record
Abstract
Population genetic research is a critical tool for the conservation and management of marine mammals and other species. The bowhead whale (Balaena mysticetus) is subject to aboriginal subsistence hunting in Alaska, Canada, Chukotka, and Greenland and managed by the International Whaling Commission for all those countries except Canada. Genetic studies support conservation management plans and the determination of safe hunting quotas by providing information on levels of genetic diversity, estimates of abundance and effective population size, and stock separation. Because bowhead populations are monitored in several countries, including genetic monitoring, there is a need for methods that can be consistently used in multiple labs that provide comparable data that can be publicly shared and built upon by successive studies. Here we present a new panel of single nucleotide polymorphisms (SNPs), derived from multiple bowhead populations, that meet those criteria. We describe the use of the Fluidigm SNPtype assay for analyzing 69 autosomal, 6 X-chromosome, and 1 Y-chromosome SNPs. Results indicate that the methods herein are reliable and have low error rates. Because SNPs are discrete sequence-based genetic markers, the panel of loci described here can be replicated, used in different labs, and are directly comparable, making SNPs more useful than existing microsatellite markers.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".