American Meat Science Association Guidelines for Meat Color Measurement
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 it