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Enhancing highly unsaturated ω-3 fatty acids in phase-fed rainbow trout (Oncorhynchus mykiss) using Alaskan fish oils

2010· article· en· W2158695000 on OpenAlexaff
David A.J. Stone, Alexandra Oliveira, Sébastien Plante, Scott Smiley, Peter J. Bechtel, Ronald W. Hardy

Bibliographic record

VenueAquaculture Nutrition · 2010
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAquaculture Nutrition and Growth
Canadian institutionsVancouver Coastal Health
Fundersnot available
KeywordsRainbow troutFish oilCanolaBiologyFish mealFood scienceEicosapentaenoic acidFisheryMenhadenAnimal scienceFatty acidPolyunsaturated fatty acidFish <Actinopterygii>Biochemistry

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.084
Threshold uncertainty score0.880

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.257
Teacher spread0.240 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations21
Published2010
Admission routes1
Has abstractyes

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