Enhancing land-based culture of coho salmon through genomic technologies: An economic analysis
Bibliographic record
Abstract
The selection of salmon broodstock can enhance certain economically important biological traits over generations, via the use of genomic technologies. Information related to flesh quality, disease resistance, growth rate, and feed conversion ratio, has been collected for coho salmon (Onchorhynchus kisutch) and may be applied to breeding programs in British Columbia. Marker-assisted selection (MAS) and genomic selection (GS) are two technologies used to identify breeders based on genes directly controlling performance traits. This study aims to quantify the net present value of these technologies, applied to coho salmon broodstock in recirculating land-based systems. We compute the value of these genomic technologies by taking the difference in profits for farmed coho salmon production, when the biological traits mentioned above are enhanced through selective breeding. Results indicate the value of the genomic technologies is around $700 to $6,280 per tonne of coho salmon produced, depending on the targeted trait. Flesh quality yields the greatest change in net present value, followed by growth rate. Our findings may offer a means to meet part of the growing demand for seafood through increased production of coho salmon and reinforce the importance of an ecologically sustainable and economically viable aquaculture industry in 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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 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".