The Effects of Castration, Implant Protocol, and Supplementation of Bos indicus-Influenced Beef Cattle under Tropical Savanna Conditions on Growth Performance, Carcass Characteristics, and Meat Quality
Bibliographic record
Abstract
The effects of castration, supplementation, and implant protocol (IP) on growth, carcass characteristics, and meat quality of grass-fed cattle were evaluated. Two experiments followed a two-way ANOVA and a 2 × 2 factorial arrangement. Experiment-I, 99 bulls were evaluated for: (a) supplementation (mineral (MS) or strategic protein-energy supplementation (SS), and (b) IP (repeated (day-0 and day-90) Zeranol-72 mg implantation (Zeranol–Zeranol) or Trenbolone Acetate-140 mg/Estradiol-20 mg (day-0) followed by Zeranol-72 mg (day-90) (TBA/E2–Zeranol). Experiment II, 50 animals were evaluated for: (a) IP (like Experiment-I), and (b) male class (steers vs. bulls). In Experiment-I, SS bulls had greater growth rate, carcass yield, and yield of high-valued boneless lean cuts than MS bulls, while decreasing (p < 0.05) time to harvest. Steaks from SS-bulls on TBA/E2–Zeranol IP were more (p = 0.05) tender than SS/Zeranol–Zeranol counterparts. Experiment-II bulls had greater growth than steers, but decreased (p < 0.05) carcass quality aspects. Zeranol–Zeranol increased (p < 0.01) meat tenderness of steers. Interactions (p < 0.05) affected cutability (Experiment-II) and meat sensory traits (Experiment-I/II). The SS improved growth, carcass yield, and shortened days until harvest of bulls, while TBA/E2–Zeranol IP positively affected tenderness in bull meat only. Castration improved carcass quality while the implant effects on cutability and tenderness were male-class dependent.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".