A method of production of boneless chicken wings (drumettes and winglets) by separation of periosteum from bone without cutting skin and muscles
Bibliographic record
Abstract
The deboning of broiler chicken wings, including drumettes and winglets, is not common in the poultry processing industry. However, consumers who like convenient foods may be interested in boneless products. Samples of broiler wings were deboned by articular cartilage dislocation and periosteum stripping without cutting skin and muscles to obtain boneless drumettes and winglets, with each having inner space formed by bone removal. The average weight of bone-in winglets (30.7 g) was less (P < 0.05) than that of bone-in drumettes (39.9 g), whereas the average percentage of boneless product was less (P < 0.05) in the drumettes (74.9) than in the winglets (80.1). There was a smaller number of muscles in the drumettes than in the winglets, but major muscles in the drumettes were larger than any muscles in the winglets. The average weight of muscle was greater (P < 0.05) and that of skin was less (P < 0.05) in the drumettes than in the winglets, and thus the muscle/skin ratio was approximately twice as high (P < 0.05) in the drumettes. The size and shape were different between the bone-in and boneless products, as expected. When a cooked product was examined, no appreciable inner space (resulting from bone removal) was seen on its transverse section. The advantages of boneless wing products over bone-in wing products were discussed. It was concluded that the method described in the present study is useful for the production of high-quality boneless wing products.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".