A survey of broiler breast meat quality in the retail market of Quebec
Bibliographic record
Abstract
In this study, 206 breast fillets were purchased from grocery stores in the province of Quebec and evaluated for the presence of different quality defects. Of these fillets, 48.5% showed breast muscle myopathies (BMM), 19.4% showed pale, soft, and exudative (PSE), and 6.8% showed dark, firm, and dry (DFD) attributes. BMM were equally present ( P > 0.05) in fillets of economical, commercial, and high-quality brands, while PSE-like fillets were more present in economical brands ( P < 0.0001). The combined effect of BMM and DFD induced significantly higher counts of Salmonella ( P = 0.03) and Enterobacteriaceae ( P = 0.03) in myopathic than in unaffected fillets. These quality defects also altered the nutritional quality of breast meat: BMM-affected fillets had greater fat content ( P < 0.0001) and DFD fillets had lower protein content ( P = 0.041) than normal fillets. The technological quality was only slightly impacted by BMM, while PSE-like fillets had higher cooking loss ( P = 0.009) and a tougher texture after cooking ( P < 0.0001) than DFD fillets. For the first time, this study confirmed the presence of multiple quality issues in the Quebec poultry supply chain, and provided valuable data to support future research efforts.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".