Comparison of in vivo and in silico growth performance and variability in pigs when applying a feeding strategy designed by simulation to control the variability of slaughter weight
Bibliographic record
Abstract
Variability in bodyweight (BW) among pigs complicates the management of feeding strategies and slaughter. Including variability among individuals in modelling approaches can help to design feeding strategies to control performance level, but also its variability. The InraPorc model was used to perform simulations on 10 batches of 84 crossbred pigs each to characterise the effect of feeding strategies differing in amino acid supply or feed allowance on the mean and variation in growth rate. Results suggested that a feed restriction reduces the coefficient of variation of BW at first departure for slaughter (BW1) by 34%. Growth performance obtained from an in silico simulation using ad libitum and restricted feeding plans was compared with results obtained in an in vivo experiment on a batch of 168 pigs. Pigs were offered feed ad libitum or were restricted (increase in feed allowance by 27 g/day up to a maximum of 2.4 and 2.7 kg/day for gilts and barrows, respectively). A two-phase feeding strategy was applied, with 0.9 and 0.7 g of digestible lysine per MJ of net energy (NE) in diets provided before or after 65 kg BW, respectively. Actual growth was similar to that obtained by simulation. Coefficient of variation of BW1 was similar in vivo and in silico for the ad libitum feeding strategy but was underestimated by 1 percentage point in silico for the restriction strategy. This study confirms the relevance of using simulations performed to predict the level and variability in performance of group housed pigs.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".