A dynamic model of protein digestion in the small intestine of pigs.
Bibliographic record
Abstract
A dynamic mathematical model of the digestion of proteins in the small intestine of pigs was developed. The model integrates current knowledge on the transit of digesta along the small intestine, endogenous secretions, digestion of proteins, and absorption of amino acids into a mechanistic representation of digestion. The main characteristics of the model are the following: the small intestine is divided into several segments of variable length but with equal digesta retention time; the rate of transfer of digesta between segments is based on the progression of myoelectric migration complexes; pancreatic and biliary secretions are poured into the first segment, whereas intestinal secretions enter all intestinal segments; protein hydrolysis is described by first-order equations; and an intestinal absorption capacity is used to estimate absorption of hydrolyzed protein. Simulation results are consistent with observed data, although more information is needed to represent reality more closely. The sensitivity analysis shows that parameters for protein hydrolysis largely determine protein digestibility. The absorption capacity of the small intestine limits the absorption of amino acids at the beginning of a meal and modulates the appearance of amino nitrogen in the portal vein. It also shows that amino acid absorption can be limiting to protein digestibility when large amounts of protein are eaten in a single daily meal. The model is useful in evaluating the dynamics of protein digestion and absorption of feedstuffs. The model can be used in evaluating protein digestion of different feedstuffs and feeding strategies.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".