Modelling growth and body composition in fish nutrition: where have we been and where are we going?
Bibliographic record
Abstract
Mathematical models in fish nutrition have proven indispensable in estimating growth and feed requirements. Nowadays, reducing the environmental footprint and improving product quality of fish culture operations are of increasing interest. This review starts by examining simple models applied to describe/predict fish growth profiles and progresses towards more comprehensive concepts based on bioenergetics and nutrient metabolism. Simple growth models often lack biological interpretation and overlook fundamental properties of fish (e.g. ectothermy, indeterminate growth). In addition, these models disregard possible variations in growth trajectory across life stages. Bioenergetic models have served to predict not only fish growth but also feed requirements and waste outputs from fish culture operations. However, bioenergetics is a concept based on energy-yielding equivalence of chemicals and has significant limitations. Nutrient-based models have been introduced into the fish nutrition literature over the last two decades and stand as a more biologically sound alternative to bioenergetic models. More mechanistic models are required to expand current understanding about growth targets and nutrient utilization for biomass gain. Finally, existing models need to be adapted further to address effectively concerns regarding sustainability, product quality and body traits.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".