Short communication: Using infrared ocular thermography as a tool to predict meat quality from lean cattle breeds prior to slaughter: Exploratory trial
Bibliographic record
Abstract
Aim of study: To assess the potential of using infrared ocular thermography (IROT) as a tool to predict beef quality at the slaughterhouse.Area of study: The study was carried out in the Salteras’s slaughterhouse (Seville, Spain).Material and methods: Ocular temperature images were captured from 175 lean young bulls prior to slaughter. Carcasses were classified into three groups according to weight: ˂250 kg, 250-310 kg and ˃310 kg. IROT was measured just before slaughter and pH was measured 24 h later. Colour parameters (CIELAB space) were evaluated 48 h post-slaughter. Water holding capacity was evaluated at seven days after slaughter.Main results: IROT mean values were higher in heavier bulls (p<0.05), probably due to these animals appeared to movilize a greater blood flow, thus increasing ocular temperature. Furthermore, IROT showed a statistically significant correlation with both pH from light carcasses (r=0.66; p<0.001), and mean Hue value from all carcass weights (r=-0.22; p<0.05). A quadratic regression analysis accounting carcass weight as a continuous variable, found medium to strong fit values for pH (R2=0.52; RMSE=0.032; p<0.01) and medium fit values for H* (R2=0.41; RMSE=3.793; p<0.001), changing their relation with IROT depending on carcass weight.Research highlights: IROT showed potential to become a useful tool to assess pH in light carcasses and to assess H* in all carcasses of young bulls prior to slaughter, regardless their weight. However, further studies would be recommended under more variable pre-slaughter stress conditions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".