Data from: Detection of outlier loci and their utility for fisheries management
Bibliographic record
Abstract
Genetics-based approaches have informed fisheries management for decades, yet remain challenging to implement within systems involving recently diverged stocks or where gene flow persists. In such cases, genetic markers exhibiting locus-specific (“outlier”) effects associated with divergent selection may provide promising alternatives to loci that reflect genome-wide (“neutral”) effects for guiding fisheries management. Okanagan Lake kokanee (Oncorhynchus nerka), a fishery of conservation concern, exhibits two sympatric ecotypes adapted to different reproductive environments, however, previous research demonstrated the limited utility of neutral microsatellites for assigning individuals. Here, we investigated the efficacy of an outlier-based approach to fisheries management by screening >11,000 expressed sequence tags for linked microsatellites and conducting genomic scans for kokanee sampled across seven spawning sites. We identified eight outliers among 52 polymorphic loci that detected ecotype-level divergence, whereas there was no evidence of divergence at neutral loci. Outlier loci exhibited the highest self-assignment accuracy to ecotype (92.1%), substantially outperforming 44 neutral loci (71.8%). Results were robust among-sampling years, with assignment and mixed composition estimates for individuals sampled in 2010 mirroring baseline results. Overall, outlier loci constitute promising alternatives for informing fisheries management involving recently diverged stocks, with potential applications for designating management units across a broad range of taxa.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.011 |
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".