A comparison of stock and individual identification for sockeye salmon (Oncorhynchus nerka) in British Columbia provided by microsatellites and single nucleotide polymorphisms
Bibliographic record
Abstract
Variation at 14 microsatellite loci, one major histocompatibility complex (MHC) locus, and 49 single nucleotide polymorphism (SNPs) loci was surveyed in 44 populations of sockeye salmon ( Oncorhynchus nerka ) over 16 regions from southern and central British Columbia, Canada. Sequential addition of the five highest rated SNPs to the suite of 14 microsatellites provided the equivalent average accuracy when compared with the current suite of microsatellites and MHC. Six microsatellites provided the equivalent average stock identification resolution and individual assignment accuracy compared with 46 SNPs. For regional stock compositions, 53–104 SNPs were projected to be required to provide accuracy and precision equivalent to the microsatellites. For population-specific stock compositions, 75–79 SNPs were projected to be required to provide accuracy and precision equivalent to the microsatellites. Equivalency in individual assignment accuracy to region was estimated to require 100 SNPs of the quality evaluated in the study, whereas equivalent accuracy in assignment to specific populations was estimated to require 124 SNPs. Applications that incorporate the existing power of a combined microsatellite–SNP approach are the best current technique available for sockeye salmon stock identification applications in southern British Columbia.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".