Heart Fatty Acid Binding Protein Gene for Improving Meat Quality
Bibliographic record
Abstract
Intramuscular fat and meat quality Intramuscular fat (IMF) content is attracting increasingly more attention in swine breeding especially because it is also positively correlated with eating attributes of pork, such as juiciness, tenderness and flavour (Bejerholm and Barton-Gode 1986; Eikelenboom et al. 1996). Pork consumers have clearly demonstrated a preference for intramuscular fat when rating pork in blind taste panel tests (NPPC 1996). Research has shown that average IMF measured through chemical analysis lines up very well with marbling scores. Marbling is also negatively related to the incidence of pale, soft, exudative (PSE) pork (Jones et al., 1994). Pork loins must have at least 2 % fat in lean meat, else, the cooked meat will be too dry and tasteless (Meadus, 2000). In earlier studies, Bejerholm and Barton-Gode (1986) identified a threshold value of 2 % intramuscular fat for optimal tenderness. According to a US study (De Vol et al., 1988), the threshold level was 2.5-3%. European scientists also believe at least 2 % IMF is needed to produce consumer acceptable pork loins (See et al. 1995). In Canada, retailers would prefer to have loins with more marbling, according to
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".