Genetic differentiation in walleye pollock (<i>Theragra chalcogramma</i>) in response to selection at the pantophysin (<i>Pan</i>I) locus
Bibliographic record
Abstract
Samples of walleye pollock (Theragra chalcogramma) from the North Pacific Ocean and Bering Sea were screened for variation at the pantophysin (PanI) locus. Global genetic differentiation across samples (FST = 0.038) was considerably greater than reported in previous population studies using allozymes, mtDNA, or microsatellite loci and significantly greater than FSTdistributions of neutral loci simulated over a large range of locus heterozygosity. PanI allele frequencies varied over a broad latitudinal gradient and were correlated with estimated mean surface temperatures, resulting in the greatest levels of genetic divergence between the northern Bering Sea and the southernmost locations in the temperate Pacific Ocean (Puget Sound, Japan). The discordance between estimates of population differentiation estimated from PanI and other neutral marker classes, both in magnitude and in geographic patterns, could arise from temperature-mediated effects of natural selection over broad geographic scales. Our empirical results suggest that loci subject to directional selection may prove to be useful markers for stock identification in weakly structured marine fishes.
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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 | 0.000 |
| 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) | 0.000 | 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".