Utility of genetically based health indicators for selection purposes in captive‐reared chinook salmon, Oncorhynchus tshawytscha
Bibliographic record
Abstract
Three health indicators, plasma lysozyme activity, PCR-based detection of Renibacterium salmoninarum (a causative agent of bacterial kidney disease), and a necropsy-based Health Assessment Index (HAI), were used to examined genetically based variation in a captive population of chinook salmon (Oncorhynchus tshawytscha W.). The study group consisted of four distinct genetic cross-types: two purebred cross-types originating from mating wild parents (Big Qualicum River, BC, Canada) and domestic parents (Yellow Island Aquaculture, Ltd, Quadra Island, BC, Canada) and two reciprocal hybrid cross-types from the mating of wild and domestic parents. Narrow-sense heritability estimates for plasma lysozyme activity and the incidence of R. salmoninarum were calculated, and the genetic correlation of health indicator response with survival and growthwas estimated. Signi¢cant di¡erences among cross-types were found for plasma lysozyme activity, HAI, survival after a natural outbreak of vibriosis (but not after a vibriosis disease challenge), relative growth rate, size-at-age (420 and 615 days post fertilization), and R. salmoninarum presence. Despite a signi¢cant sire component of heritability for plasma lysozyme activity, the lack of significant heritability estimates for R. salmoninarum presence, and non-significant genetic correlations with performance variables indicates that selection to improve the health status of fish stock using the three health indicators examined here would likely not result in a measurable correlated response in survival or growth.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".