Large-scale parentage-based tagging and genetic stock identification applied in assessing mixed-stock fisheries and hatchery brood stocks for coho salmon in British Columbia, Canada
Bibliographic record
Abstract
Direct DNA sequencing is powering a revolution in the application of genetics to resource management, with parentage-based tagging (PBT) increasingly applied to salmon fisheries and hatchery brood stock management and assessment. Genetic stock identification (GSI) and PBT were applied to assessment of 2018 coho salmon (Oncorhynchus kisutch) ocean fisheries and hatchery brood stocks in British Columbia (BC), Canada, with 6391 individuals successfully genotyped in fishery samples and 7805 individuals genotyped in 40 hatchery brood stocks. Population-specific contributions to mixed-stock fisheries and exploitation rates were estimated with coded-wire tags (CWTs) and GSI–PBT technologies for six populations. PBT assignments, verified by CWTs, were 100% accurate for 308 individuals with respect to population of origin and age. There was generally reasonably close agreement of estimated population-specific exploitation rates between CWT and genetic methods. We conclude that a genetic approach can improve upon the results available from the current CWT program for assessment and management of coho salmon fisheries and hatchery brood stocks in BC and provide information critical to aid in implementation of Canada’s Policy for Conservation of Wild Pacific Salmon.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".