Effect of Different Finishing Strategies and Steer Temperament on Animal Welfare and Instrumental Meat Tenderness
Bibliographic record
Abstract
The aim of this experiment was to evaluate the effect of different fattening systems from pasture to concentrate and temperament on animal welfare (AW) and meat quality (MQ). Eighty-four Hereford steers were randomly assigned to the following groups: T1, pasture (4% of animal live weight: LW); T2, pasture (3% LW) plus concentrate (0.6% LW); T3, pasture (3% LW) plus concentrate (1.2% LW); T4, an ad libitum concentrate treatment. Temperament was assessed by three individual tests: crush score, flight time, and exit speed, building a multicriterial temperament index (TIndex). The flight zone was also registered for each treatment. AW was assessed through the integration of indicators of productivity, physiology, and behavior, as well as by monitoring the health status within each treatment. Shear force was registered for MQ. Differences in average daily gain were due to the different energetic composition of the diets (T4 > T3 > T2 > T1) and were not attributable to animal welfare problems. Animals from T4 had the higher average daily gain (ADG) but welfare was negatively affected, being evident through physiological indicators, the restriction or deprivation of relevant behaviors, diet-related diseases, and mortality. T1, T2, and T3 did not appear to compromise animal welfare. However, strict preventive measures and monitoring should be taken during the habituation process and when using any new diet that includes concentrate, because of possible dietary diseases. Shear force values were lower in T1. None of the animals in our experiment were excitable or aggressive, but there was a positive response to handling in all treatments. In addition, regardless of diet, calmer animals had higher average daily gain and lower shear force values; thus, temperament appears to have a significant influence on productivity and meat 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 | 0.000 | 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 | 0.000 | 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".