Pre-slaughter road transportation times: meat quality and sensory properties of Nellore bull meat
Bibliographic record
Abstract
This study evaluated the effects of different pre-slaughter road transportation times on sensory evaluation and instrumental measurements of Nellore cattle meat quality. About 15 farms were classified according to transportation times (five farms per transportation time group) as follows: 1 — less than 2 h (<2), 2 — between 2 and 4 h (>2 and ≤4), and 3 — between 4 and 6 h (>4 and ≤6). The experiment was a completely randomized design with 15 replications and three treatments. Meat analyses were performed on steaks taken from the longissimus dorsi muscle from the 10th rib to the third lumbar vertebra. Road transportation times had no significant effects on pH and mean shear force values. Sarcomere length, myofibrillar fragmentation index, and creatine kinase levels were also not affected by transportation time. Furthermore, road transportation times had no effect on meat tenderness, flavour, odour, and overall acceptability when evaluated by consumers. Thus, a pre-slaughter road transportation time up to 6 h does not affect the sensory evaluation and instrumental measurements of Nellore cattle meat quality when raised using a grazing system.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".