PSIII-2 Infrared technology: A potential tool for improved pork production
Bibliographic record
Abstract
Abstract This study investigated infrared technology (IRT) as a non-invasive tool for identifying febrile or stressed pigs before slaughter. A total of 120 market pigs (BW= 105.1 ± 4.9 kg) were transported in five replicate trips (20–25 pigs/replicate) for ~2 hr to an abattoir during summer 2020. Ocular and body temperatures of the pigs were recorded using a consumer grade digital infrared camera (FLIR C3, FLIR Systems) in lairage immediately after transport. Thermographic images were taken from 0.25m and 2m from pigs’ eyes and body (back/flank), respectively. At slaughter, blood samples were collected from each animal for cortisol, glucose and lactate analyses. Carcass pH measures were taken at 1 and 3 h post-mortem and loin samples were collected for meat quality assessment. Linear regression models (SAS 9.4) were used to evaluate whether post-transport temperature was predictive of blood and meat quality responses using ambient temperature and relative humidity as covariates. There were significant relationships between pigs’ ocular and body temperatures and blood measures. The regression coefficient (r2) of ocular temperatures for blood cortisol, glucose and lactate were 0.15, 0.15 and 0.04, respectively (P ≤0.03). There were significant relationships between pig ocular temperatures and pH taken at 1, 3 and 24 h post-mortem (r2=0.32, 0.18 and 0.51 respectively, P < 0.001). Meat yellowness (b*) and drip loss increased with body temperatures [r2=0.12 (P =0.0002) and 0.05 (P = 0.024), respectively]. Results for Warner-Bratzler shear force showed that higher temperatures were associated with tougher meat (ocular temperature: r2=0.51, P < 0.0001). In conclusion, as pig temperature increased, blood stress markers and drip loss increased and pH at 1 and 3 h post-mortem decreased, indicative of pale, soft and exudative (PSE) meat traits. IRT shows potential for identifying diseased/stressed pigs prior to slaughter and could be a valuable tool for improved food safety and meat 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".