Population and individual identification of Chinook salmon in British Columbia through parentage-based tagging and genetic stock identification with single nucleotide polymorphisms
Bibliographic record
Abstract
A study was undertaken to evaluate whether a parentage-based tagging (PBT) and genetic stock identification (GSI) program has the potential to emulate the results from an existing coded-wire tag (CWT) assessment program in British Columbia. A PBT–GSI approach was used to identify Chinook salmon (Oncorhynchus tshawytscha) to specific populations and brood years where 36 241 individuals from 45 populations were genotyped at 321 single nucleotide polymorphisms (SNPs). Known-origin and known-age age 1 juveniles from seven test populations were assigned via PBT (two parental genotypes required, 538 of 656 juveniles assigned; one parental genotype required, 636 of 656 juveniles assigned) with a minimum accuracy of 99.9%. Assignment accuracy via PBT of 1026 ages 1, 2, or 3 Chinook salmon returning to nine populations in 2015 or 2016 (two parental genotypes required, 556 of 1026 individuals assigned; one parental genotype required, 898 of 1026 individuals assigned) was a minimum of 99.8%. A PBT–GSI or PBT system of identification may provide an alternate cost-effective method of identification in the assessment and conservation of Canadian-origin Chinook salmon relative to the existing CWT program, thereby providing very high resolution of mixed-stock fishery samples containing both hatchery-origin (adipose fin clipped) and wild-origin (unclipped) populations.
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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 | 0.001 | 0.002 |
| 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".