The Effect of On-Farm Slaughter via Gunshot and Conventional Slaughter on Sensory and Objective Measures of Beef Quality Parameters
Bibliographic record
Abstract
Beef quality parameters can be negatively affected by pre-slaughter stress. Slaughter via gunshot directly on the pasture appears to be suitable for the reduction of pre-slaughter stress by avoiding stressors such as transportation, lairage and human contact. The effect of slaughtering via gunshot on sensory and objective measures of beef quality parameters for the Musculus longissimus dorsi of Galloway steers was analyzed and compared to conventional slaughter at the abattoir using captive-bolt stunning. The Warner-Bratzler shear force (WBSF) was significantly (P < 0.01) lower for the meat of the animals slaughtered via gunshot (arithmetic mean (AM) gunshot: 4.34 kg; AM captive-bolt pistol: 4.77 kg). However, trained assessors were not able to recognize this difference (P > 0.05). No significant differences (P > 0.05) were observed for cooking loss and the sensory quality evaluation of juiciness. As measured by the WBSF, the meat of the animals slaughtered via gunshot was slightly more tender than was the meat of the animals stunned with a captive-bolt pistol. However, for the cooking loss and sensory evaluations, no effect of the slaughter methods was observed. Nevertheless, this study reveals the potential that slaughter via gunshot provides for the improvement of beef quality.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".