Effects of plant and dairy proteins on the texture and microstructure of lean turkey meat batters
Bibliographic record
Abstract
The effect of using non-meat proteins (pea, faba, rice, whey, and caseinate; 2% level) on the texture, yield, and structure of lean turkey meat batters was compared to an all-meat control and a control with 2% added meat proteins. The best overall proteins were caseinate (animal derived) and pea (plant derived) which reduced cooking loss (P < 0.05, 60% compared to the two controls), while also increasing hardness over the first control treatment. Rice protein also increased hardness (P < 0.05) but did not reduce cooking loss compared to the first control. Part of this could also be seen under the microscope, where the caseinate and faba treatments showed denser microstructure compared to the rice and whey protein treatments; both had higher cooking loss. Overall, the meat industry is continuously searching for non-meat ingredients to enhance texture and yield and this study provides ranking of some new protein preparations.
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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 | 0.000 | 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) | 0.001 | 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".