The effect of ractopamine supplementation at 5 ppm of swine finishing diets on growth performance, carcass composition and ultimate pork quality
Bibliographic record
Abstract
Five hundred thirty-six finishing pigs were placed on experiment for an average of 26 d prior to slaughter, to determine the effects of 5 mg ractopamine hydrochloride (RAC) per kg of diet on growth performance, carcass composition and the eating quality of pork. Treatments included a control diet similar to a western Canadian commercial finishing diet and a treatment diet containing 5 mg RAC kg-1 with elevated amino acids, vitamins and minerals. The experiment started when average animal weight was 86 kg; pigs were marketed at an average liveweight of 118 kg. Two pigs were selected from each of 32 pens in week 4 of the experiment, for detailed carcass and meat quality evaluation, providing 16 pigs per gender per treatment. RAC-fed pigs reached market weight 4 d sooner (P < 0.05), grew 13 % faster (P < 0.05) and had 13% better feed efficiency (P < 0.05) than the controls. RAC-fed pigs also had 1 mm less backfat and 2.5 mm thicker loins (P < 0.05). Ultimate pH, purge loss and visual colour scores were unaffected by treatment but RAC-fed pigs had lower CIE a* and b* measurements (P < 0.05). RAC had no effect on juiciness, flavour, saltiness or overall acceptability (P > 0.10), but increased Warner-Bratzler shear force and reduced taste panel tenderness slightly (P < 0.05). The inclusion of 5 mg RAC kg-1 in a commercial finishing diet will increase the rate and efficiency of gain and improve carcass composition. Minimal impact on pork quality can also be expected with the use of RAC fed at this level. Key words: Ractopamine, swine, carcass composition, pork quality, beta-adrenergic agonist
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".