Toward Precise Nutrient Value of Feed in Growing Pigs: Effect of Meal Size, Frequency and Dietary Fibre on Nutrient Utilisation
Bibliographic record
Abstract
Nutritional values of ingredients have been and still are the subject of many studies to reduce security margins of nutrients when formulating diets to reduce feed cost. In most studies, pigs are fed a limited amount of feed in a limited number of meals that do not represent how pigs are fed in commercial farm conditions. With free access to feed, pigs follow their intrinsic feeding behaviour. Feed intake is regulated by satiety and satiation signals. Reducing the feed intake level or feeding frequency can affect digestibility and transit time and induce metabolic changes. To reduce feed costs, alternative ingredients that are frequently rich in dietary fibre are added to diets. Fibre acts on the digestion process and transit time by decreasing energy density and causing viscosity. Various analyses of fibre can be realised, and the measured fibre fraction can vary. Exogenous enzymes can be added to counteract the effect of fibre, but digestive tract conditions, influenced by meal size and frequency, can affect the efficiency of supplemented enzymes. In conclusion, the frequency and size of the meals can affect the digestibility of nutrients by modulating gastrointestinal tract conditions (pH and transit time), metabolites (glucose and short-chain fatty acids) and hormones (glucagon-like peptide 1 and peptide tyrosine tyrosine).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".