Better Defining Nutritional Requirements of Fish and the Nutritive Value of Feed Ingredients: Lessons from Integration of Experimental Data from a Wide Variety of Sources
Bibliographic record
Abstract
Each and every year hundreds of studies are published on the nutritional requirements of aquatic animals and the composition and nutritive value of different feed ingredients. However, knowledge integration efforts carried out in recent years at the UG/OMNR Fish Nutrition Research Laboratory have indicated that, in general, less than 50% of the published studies have appropriate experimental design, contain sufficient information or have results deemed sufficiently meaningful and credible results to be used in statistical meta-analyses. Small but meaningful improvements in the objectives, scope, and experimental design of research trials, as well as, in the quality and completeness of information reported in scientific papers could greatly improve the quality and relevance of research efforts in aquaculture nutrition.Research trials should be designed with the perspective of potential end users (e.g. feed manufacturers) in mind. Nutrient requirements trials should not ideally have fewer than six (6) graded levels of the nutrient studied and report sufficient information on the composition and digestibility of the experimental diets, and growth performance and body composition of the experimental animals. In the case of research on the nutritive value of feed ingredients, more emphasis should be on assessment of available nutrient “contributions” of ingredients to the diet (i.e. the bioavailability of nutrients in ingredients) rather than “absence of effect” of test ingredients when included in luxurious (e.g. high fish meal) diets. Finally, nutritional models and data integration and analysis systems should be developed and widely used to more effectively compile, analyze, interpret and valorize information generated by aquaculture nutrition research efforts carried out around the world.
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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 | 0.158 | 0.200 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.008 | 0.018 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".