Characteristics of cold-induced dark, firm, dry broiler chicken breast meat
Bibliographic record
Abstract
1. A study was designed to characterise dark, firm, dry (DFD) breast meat resulting from cold exposure of broilers and compare its properties with normal breast meat from cold-stressed and control birds. 2. A total of 140 broilers were selected from 5- and 6-week-old birds exposed to cold temperatures ranging from -18 to -4°C, or a control temperature of +20°C for 3 h in an environmental chamber. Half of these birds were slaughtered immediately following the cold exposure and the other half were given 2 h of lairage. 3. Breast meat samples were categorised based on ultimate pH (pH(u)) and colour L* (lightness) values into normal (5·7 ≤ pH(u)≤ 6·1; 46 ≤ L* ≤ 53) breast meat from control (control-normal) or cold-stressed (cold-normal) birds, and DFD (pH(u) > 6·1; L* < 46) breast meat, which only occurred in cold-stressed birds (cold-DFD). 4. Residual glycogen was not different between cold-DFD and control-normal breast meat. Lactate concentration was lower in cold-DFD compared with control-normal breast meat. Lactate concentration almost tripled for all the samples by 30 h post-mortem, which resulted in a drop in pH of normal meat, but did not have any effect on pH of DFD breast meat. Glycolytic potential at both 5 min and 30 h post-mortem was lower in DFD breast meat compared with the normal breast meat from both cold-stressed and control birds. 5. Cold-DFD breast meat was significantly darker, with higher pH(u), lower cook loss, higher water-binding capacity and processing cook yield than cold-normal and control-normal breast meat, which were not different from each other.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".