A dynamic model of nutrient pathways, growth, and body composition in fish
Bibliographic record
Abstract
The growth and body composition of an organism are dynamic and depend on available diet, as well as other environmental variables. A structured model is described predicting growth, body composition, and the first limiting amino acid for a variety of feeding regimes over a relatively long time scale. This model continuously provides, as a function of time, the flow of nutrients and metabolites through the principal metabolic pathways leading to tissue growth. Measurements of growth and whole body composition (including amino acid composition) from a large-scale experiment with Atlantic salmon (Salmo salar) have been employed to calibrate this model. Comparisons of simulations with the results of feeding experiments validate the predictive ability of the model. These types of simulations will be valuable in studying the quantitative relationships between intracellular energy levels and nutrient distribution in tissues. They will also provide data for balancing diet composition and amino acid profile in order to optimize growth with respect to factors such as body weight, body composition, and ambient temperature. As a result of its modularity, the model can be easily extended to include additional physiological and metabolic processes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".