Animal Welfare Assessment and Meat Quality through Assessment of Stress Biomarkers in Fattening Pigs with and without Visible Damage during Slaughter
Bibliographic record
Abstract
The study aimed to investigate the physiological and meat quality differences between Non-Ambulatory, Non-Injured (NANI), and without apparent abnormalities (non-NANI) pigs in a commercial slaughterhouse setting, focusing on the impact of stress and health conditions on the overall well-being and meat quality of the animals. A total of 241 surgically castrated crossbred male pigs from Southern Brazil were analyzed, with 131 non-NANI pigs and 110 NANI pigs. Infrared orbital temperature, rectal temperature, hematological parameters, and meat quality measurements were collected. Statistical analysis included ANOVA tests and principal component analysis (PCA). NANI pigs exhibited significantly higher infrared orbital temperatures and rectal temperature (p < 0.01). Hematological analysis revealed higher levels of hemoglobin, hematocrit, and red blood cells in NANI pigs (p < 0.05). White blood cell count and lactate dehydrogenase were significantly elevated in NANI pigs (p < 0.01), indicating potential infections or inflammatory responses. Meat quality parameters showed that NANI pigs had lower pH values, higher luminosity, and increased drip loss (p < 0.01), reflecting poorer water retention and potential muscle glycogen depletion. The study highlights the physiological and meat quality differences between NANI and non-NANI pigs, emphasizing the impact of stress, health conditions, and handling procedures on the animals. Blood biomarkers proved valuable in assessing physiological stress, immune response, and potential health issues in pigs, correlating with meat quality abnormalities. Utilizing these biomarkers as predictive tools can enhance animal welfare practices and contribute to improving meat quality in the swine industry.
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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 | 0.001 | 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) | 0.001 | 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".