Economic values of supply chain productivity and quality traits calculated for a farmed European whitefish breeding program
Bibliographic record
Abstract
Economic values of different fish traits are needed to direct breeding programs to optimize economic benefits for aquaculture industry. The aim of this article is to highlight and calculate how different traits affect the value of farmed fish supply chain. Supply chain approach is needed to calculate economic impact of fish traits because several fish traits affect costs and returns not only in fish farming but also at the processing and retail level. In this article, economic values are calculated for 14 productivity and product quality traits in European whitefish (Coregonus lavaretus). Productivity affecting traits, such as growth, mortality and different yields, are included in the study. In addition, economic values are calculated for several quality traits like fillet gaping, appearance and fat content of flesh.Productivity traits had the highest economic importance if the traits could be improved. However, quality traits may cause even higher economic losses, if the quality decreases. Thus, the management of the breeding program should pay simultaneous attention to both quality and productivity traits.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 teacher head, 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".