Research Note: Producing lean poultry meat composite gels with different carbohydrate-based breadcrumbs
Bibliographic record
Abstract
The effects of using different breadcrumbs (3 wheat, 2 gluten-free: pea and rice) to improve yield and modify the texture of a lean poultry product were evaluated. All breadcrumbs significantly reduced cooking loss (P < 0.05), with one of the wheat crumbs showing the best results (35% reduction). Light microscopy revealed that breadcrumbs were well connected to the cooked meat matrix, and fully hydrated, that is, forming well-structured composite gels. The presence of breadcrumbs resulted in lower hardness, chewiness, and gumminess values of 3 of the treatments (compared to the control with no breadcrumbs), while the other 2 did not affect those parameters. Overall, selected breadcrumbs that are commonly used in ground beef products can be employed to improve yield/modify texture in poultry products but have to be selected to address certain needs.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".