Omics Approaches to Meat Quality Management
Bibliographic record
Abstract
Meat quality is not one single value, but encompasses a diverse array of characteristics relating to eating quality, nutritional level, and technological performance; broader denitions can also include safety aspects. For the consumer, sensory characteristics including appearance, color, and palatability (tenderness, texture, juiciness, and ¯avor) are key (McIlveen and Buchanan 2001, Verbeke et al. 2009), while nutritive aspects include composition, protein content, fatty acid prole, and mineral level (Leheska et al. 2008, Scollan et al. 2005). In terms of sensory acceptability, which attribute is most important depends on geographic location (Aaslyng et al. 2007), but tenderness is usually considered the biggest driver (Aaslyng 2009). When most products are tender, juiciness and ¯ avor play bigger roles in liking (Miller et al. 2001). The technological aspects of quality center round the ability of muscle to interact with processing conditions to produce optimal fresh and processed products. The ability of the muscle to bind water, the pH in the early postmortem period, and the ultimate post-rigor pH are valuable indicators of technological performance (Poso and Puolanne 2005).
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.008 |
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".