Causes and Contributing Factors to “Dark Cutting” Meat: Current Trends and Future Directions: A Review
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
Dark cutting in beef and sheep meat has been the subject of extensive research with numerous associations established between it and various production practices. Despite these associations, dark cutting still occurs and causes significant financial losses globally in the fresh meat market. Consumers tend to reject dark meat as it is perceived to be from old or poorly-handled animals and is described as being tough, having an undesirable flavor, and having a short shelf-life. There is no universal system to categorize dark cutting carcasses and meat across countries, although various methods are used to determine the phenomenon. Classifying carcasses as dark cutters on the basis of ultimate pH or color using one muscle, such as the m. longissimus thoracis can lead to mis-description of other muscles within the same carcass and loss of income across the supply chain. The purpose of this review was to identify the factors predisposing animals to dark cutting and to provide recommendations and directions for future research. The review revealed no single production factor causing dark cutting, but that a range of factors or a combination of factors and interactions lead to its occurrence. Dark cutting is a complex condition that can be resolved through comprehensive management of animals, and management of human involvement, and clear guidelines to minimize the incidence of "dark cutting" meat and to improve the profitability of all sectors in the supply chain are provided here.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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