Comparing three textural measurements of chicken breast fillets affected by severe wooden breast and spaghetti meat
Bibliographic record
Abstract
In this study, we compared three popular textural tests: the compression, Meullenet–Owens razor blade (MORS), and Allo–Kramer (AK) tests, which are used to detect the wooden breast (WB) and spaghetti meat (SM) myopathies. A total of 209 fillets (71 WB, 71 SM, 67 normal) were selected from three different flocks at a large commercial plant. Thawed fillets were subjected to 20% compression tests before and after cooking, and cooked samples were subjected to the MORS and AK tests. The compression test on raw samples showed that normal and SM fillets had lower force (5.61 and 4.69 vs. 9.52 N), work (25 and 22 vs. 45 N mm), and Young’s modulus (2.71 and 2.11 vs. 4.29 N/s, p < .001) values than those of WB. The results of the compression test were confirmed by the cooked fillet results. The MORS test showed that SM had lower shear force (12.8 vs. 14.7 N) and work (249 vs. 288 N mm) values than those of the normal fillets, while WB showed intermediate values. The AK test results showed that SM had lower shear force (10.5 vs. 14.5 N) and Young’s modulus (31.0 vs. 46.0 N/s; p ≤ .01) values than those of WB fillets, whereas normal fillets had intermediate values. The compression test can be used to identify WB in both raw and cooked meat. The MORS test could distinguish cooked SM fillets from normal fillets, whereas the AK test differentiated SM from WB.HIGHLIGHTS This study compared the compression, Meullenet–Owens razor blade, and Allo–Kramer tests for detecting wooden breast (WB) and spaghetti meat (SM). The compression test identified WB in both raw and cooked fillets. Meullenet–Owens razor blade test distinguished SM from normal fillets. Allo–Kramer test accurately distinguished SM from WB fillets.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".