Relationships between temperament, meat quality, and carcass traits in Nellore cattle1
Bibliographic record
Abstract
The aim of this study was to evaluate the relationship between temperament in Nellore bulls with carcass and meat quality traits. In total, 1,400 bulls were studied, and temperament was assessed using two measurements: movement score (MOV) and flight speed test (FS). Both MOV and FS were measured at two time points, with background (MOVb and FSb) temperament measured at yearling age, ~550 d after birth, and the preslaughter (MOVps and FSps) temperament measured at the end of the feedlot period. The change of temperament resulting in an increase or decrease in reactivity was also used to measure meat quality. The traits used to define carcass and meat quality included carcass bruises (BRU), hot carcass weight (HCW, kg), ribeye area (REA, cm2), backfat thickness (BFT, cm), marbling score (MS), meat pH after thawing (pH), presence or absence of dark cutters, color parameters of luminosity (L*), redness (a*) and yellowness (b*), cooking loss (CL, %), and Warner-Bratzler shear force (WBSF, kg). A principal component (PC) analysis was initially applied to the carcass and meat quality traits, followed by logistic regression models and linear mixed models to evaluate the effects of temperament on carcass and meat quality. The risks of carcass bruises and dark cutters did not differ as a function of any temperament trait (P > 0.05). In turn, animals classified as high MOVb (reactive) had lower PC3 values (P = 0.05), CL (P = 0.02), and tended to have lower MS (P = 0.08). In addition, animals classified as high FSb (faster and reactive cattle) produced carcasses with smaller REA (P < 0.01), higher meat pH (P < 0.01), lower color gradients (L*, P = 0.04; b*, P < 0.01), and lower PC1 and PC4 scores (P < 0.01) when compared with the low FSb class. For preslaughter temperament, high MOVps was related to lower color a* (P = 0.04), whereas high FSps was related to lower HCW, MS, and PC2 (P < 0.01) than the calmer ones (low FSps). The reduction in MOV was related to more tender meat, and the reduction in FS to heavier carcass and brighter meat. We conclude that excitable temperament in Nellore cattle may have negative effects in some of the carcass and meat quality attributes assessed, mainly those related to muscle deposition on carcass and color gradients. Measurement of temperament before the cattle entered the feedlot was a better predictor of carcass and meat quality traits, compared with temperament assessment at the end of the feeding period.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".