Effects of water supplementation with tryptophan and vitamin B<sub>6</sub> or feeding hydrogenated fat on reducing hunger-induced drinking pre-slaughter in pigs
Bibliographic record
Abstract
Faucitano, L., Torrey, S., Matte, J. J., del Castillo, J. R. E. and Bergeron, R. 2012. Effects of water supplementation with tryptophan and vitamin B6 or feeding hydrogenated fat on reducing hunger-induced drinking pre-slaughter in pigs. Can. J. Anim. Sci. 92: 319–326. A current food safety challenge at pig slaughter plants comes with the presence of stomachs filled with liquid induced by hunger-related drinking in lairage. With the objective to reduce hunger-related excess drinking, 30 barrows were assigned to three treatments (10 pigs per treatment): (1) unsupplemented water or feed regimen (CONT), (2) L-Tryptophan (3 g L−1) and vitamin B6 (10 mg L−1) in the drinking water for 5d (TRP-B6), (3) hydrogenated fat (HF) supplemented at 10% in the diet for the last day of feeding before pre-slaughter fasting. As compared with CONT, neither TRP-B6 nor HF supplementation influenced behaviour in lairage and water intake at anytime over the pre-slaughter fasting period as reflected on stomach weight and its liquid content at slaughter (P>0.10). However, in HF-fed pigs plasma non-esterified fatty acids concentrations tended to be lower (P=0.09) while carcass yield was higher (P=0.04) than CONT pigs. It appears, therefore, that neither drinking water supplementation with TRP-B6 for 5 d nor feeding HF the last day before slaughter can be recommended strategies to limit excess water drinking prior to slaughter and liquid stomach content at slaughter. However, dietary HF supplementation the last day before slaughter may attenuate the effects of fasting on body energy reserves and improve carcass yield.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".