American Meat Science Association Guidelines for Meat Color Measurement
Bibliographic record
Abstract
Meat color is an important aspect of a consumer’s purchase decisions regarding meat products. Perceived meatcolor results from the interaction of light, a detector (i.e., human eye), and numerous factors, both intrinsic and extrinsic tothe muscle, that influence the chemical state of myoglobin. The complex nature of these interactions dictates that decisionsregarding evaluations of meat color be made carefully and that investigators have a basic knowledge of the physical andchemical factors affecting their evaluations. These guidelines were compiled to aid investigators in navigating the pitfalls ofmeat color evaluation and ensure the reporting of information needed for the appropriate interpretation of the resulting data.The guidelines provide an overview of myoglobin chemistry, perceptions of meat color, details of instrumentation used inmeat color evaluation, and step-by-step protocols of the most common laboratory techniques used in meat color research.By following these guidelines, results of meat color research may be more clearly presented and more easily replicated.
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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 | 0.016 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.079 | 0.058 |
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".