PSV-2 Impact of Diseases in Pig Production on Carcass and Meat Quality
Bibliographic record
Abstract
Abstract Several diseases, including Porcine Respiratory and Reproductive Syndrome (PRRS), are present in pig herds. The effects of diseases on growth performance are generally well known, but there is a lack of information on the consequences on carcass and meat quality. The aim of this project was to assess the impact of diseases present in the nursery and finishing on performances, carcass yields and meat quality. The trial was carried out at the Deschambault Swine Evaluation Station, where a project aimed at evaluating disease resilience is being carried out. A total of 480 castrated piglets from 8 different batches entered the nursery barn every 3 weeks. Pigs were observed daily, and a careful examination of the clinical signs was carried out weekly on each animal, to separate pigs showing signs of disease from those in good health. Ultrasound measurements of back fat thickness, muscle depth and intramuscular fat (IMF) level were performed three different times during growth. When the pigs reached a body weight of approximately 120 kg, carcass quality and meat yield measurements were taken 18 to 24 hours after slaughter. The ultimate pH, color, water loss and marbling were measured on the loin. The presence of PRRS and other diseases had a significant impact on the growth performance of pigs in fattening and demonstrates that the presence of the virus in the herd significantly decreases the average daily gain (ADG). This difference has a direct impact on the number of days in fattening. Results for back fat thickness at the end of fattening show significant differences (P < 0.05) between uninfected pigs (14.7 mm), pigs infected in nursery (13.9 mm) and fattening (12.8 mm) only, and pigs that have been ill both in nursery and fattening (12.4 mm). These results indicate that the presence of diseases leads to a decrease in fat deposition in pigs at the end of fattening. However, results for IMF measured at the end of fattening demonstrate that the presence of diseases had no significant impact on the final IMF level (P > 0.10), varying between 2.05% for healthy pigs and 2.13% for animals that have been sick in nursery and fattening. The results obtained in the slaughterhouse have shown that the presence of diseases does not seem to have an impact on the various measures of meat quality (P > 0.05). Only drip loss shows a significant difference (P < 0.05) between healthy and sick animals. However, the low number of sick pigs in the batches evaluated could have limited the observed effects. The work carried out by the CDPQ has made it possible to quantify the impact of diseases by providing a better understanding of their impact on product quality.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.000 |
| 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".