Use of the spectrophotometric color method for the determination of the age of skin lesions on the pig carcass and its relationship with gene expression and histological and histochemical parameters1
Bibliographic record
Abstract
The presence of lesions on the pig carcass is an indicator of poor animal welfare and has economic impact as it downgrades the carcass value. The assessment of the age of lesions on the carcass may help identify risk factors and ultimately prevent their occurrence. The aim of this study was to assess the age of lesions on pig carcasses through spectrophotometric color evaluation and to relate the results with gene expression and histological and histochemical parameters. A total of 96 barrows were mixed 4 times over 3 d before slaughter and 80 lesions were selected after skin lesion observations to define 4 age categories: < 7 h (T1), 7-25 h (T2), 25-30 h (T3), and 49-54 h (T4). A nonlesioned skin area was used as a control. At slaughter, 3 biopsies per lesion and control skin were taken immediately after bleeding for analyses of gene expression (, , , , , , , , , ), skin histological characteristics (inflammation, erosion or ulceration, and necrosis), and enzyme activity (alkaline phosphatase and adenosine triphosphatase). The number of lesions was counted on each carcass, and the color was assessed visually by a pictorial chart and instrumentally through a spectrophotometer. Delta values (Δ) were calculated as the difference between the value of the lesion and the value of the control for all measures, except for the histological analysis. Results indicated that visual color observation was not sufficiently accurate to discriminate lesions by time of infliction ( > 0.10), while the spectrophotometer ΔL* and Δa* values variation allowed the identification of < 7 h or > 25 h old lesions ( < 0.05). Similarly, the expression of , , , , and genes was higher ( < 0.05) in < 7 h old lesions, while gene expression was higher ( < 0.05) in < 25 h old lesions. As for the histological analysis, the severity of inflammation was correlated with the age of the lesion (lower score in < 7 h old lesions and higher score in > 25 h old lesions; < 0.05). To conclude, the spectrophotometric color assessment of the carcass lesions at slaughter appears to be a reliable method to discriminate between fresh and older lesions on the carcass at the abattoir.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".