Enhancing highly unsaturated ω-3 fatty acids in phase-fed rainbow trout (Oncorhynchus mykiss) using Alaskan fish oils
Bibliographic record
Abstract
Rainbow trout, average weight 185–187 g, were fed feeds containing menhaden oil, canola oil or fish oils (pollock, pink salmon or rockfish) produced from Alaskan seafood processing waste as the added oil for 8 weeks, at which time the fish weighed 391–411 g (average 404 g, pooled SE = 5.7). The fish were previously fed from 75 g average weight fed commercial feed containing poultry oil as the added oil. No significant differences were measured in final weight or feed conversion ratio among dietary treatment groups. Significant differences were found in fillet ω-3 fatty acid (FA) levels from fish receiving fish oil-supplemented feeds compared to those from fish receiving feeds containing canola oil. Fillet contents of eicosapentaenoic acid (EPA; 20:5ω3) and decosahexaenoic acid (DHA; 22:6ω3) were highest in the pollock oil treatment group, although all fish oils increased highly unsaturated ω-3 FA contents (mg 100 g−1) of fillets. Fish oil used through the production cycle was reduced by 25% by supplementing feeds with poultry oil during the middle phase of production (75–175 g) compared to using feeds containing fish oil throughout the production cycle. Fish oils recovered from Alaskan seafood processing waste were suitable alternatives to conventional fish oil as ingredients in rainbow trout production feeds.
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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 | 0.000 |
| 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) | 0.001 | 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".