A computational framework for a nutrient flow representation of energy utilization by growing monogastric animals
Bibliographic record
Abstract
A computational framework to represent nutrient utilization for body protein and lipid accretion by growing monogastric animals is presented. Nutrient and metabolite flows, and the biochemical and biological processes which transform these, are explicitly represented. A minimal set of calibration parameters is determined to provide five degrees of freedom in the adjustment of the marginal input-output response of this nutritional process model for a particular (monogastric) animal species. These parameters reflect the energy requirements to support the main biological processes: nutrient intake, faecal and urinary excretion, and production in terms of protein and lipid accretion. Complete computational details are developed and presented for these five nutritional processes, as well as a representation of the main biochemical transformations in the metabolic processing of nutrient intake. Absolute model response is determined as the residual nutrient requirements for basal processes. This model can be used to improve the accuracy of predicting the energetic efficiency of utilizing nutrient intake, as this is affected by independent diet and metabolic effects. Model outputs may be used to generate mechanistically predicted values for the net energy of a diet at particular defined metabolic states.
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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".