Effet d'une alimentation de précision sur les performances, la productivité et le coût d'alimentation des truies en gestation dans un contexte commercial de gestion des truies en groupe
Bibliographic record
Abstract
Adoption of precision feeding in pork production requires precise assessment of its potential benefits. The objective of this study was to evaluate, in a commercial setting and under group sow management, impacts of precision feeding on growth performance, productivity and feeding costs in gestating sows. Conventional feeding (C; 0.53% digestible lysine) and precision feeding (PF; digestible lysine content varied by gestation day and litter rank) were compared. Four consecutive batches in a commercial farm were studied over two complete cycles, from breeding to weaning, corresponding to 295 sows and 523 litters. For all sows, gestational muscle gain was significantly greater for PF sows. For multiparous sows, no other impact was observed, while for gilts the survival rate of piglets at birth tended to be higher for PF treatment sows (P = 0.08). Since PF covers amino acid requirements of gilts at the end of gestation better, this result seems logical. However, future studies on more animals will be needed to confirm its benefits. The results show that PF of gestating sows can reduce protein and amino acid supplies without negatively affecting the performance of sows, thus reducing the feeding cost per sow per year by €2.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".