164 Effect of trenbolone acetate, melengestrol acetate, and ractopamine hydrochloride on growth performance of growing beef cattle
Bibliographic record
Abstract
Abstract This study evaluated the effect of trenbolone acetate + estradiol implants (TBA), melengestrol acetate (MGA), and ractopamine hydrochloride (RAC) on the performance of beef cattle fed barley grain/corn silage diets. Beef cattle (279 ± 10.1 kg) were used in a complete randomized 4-yr study (yr 1, 2, n = 120 heifers and 80 steers; yr 3, 4, n = 120 heifers and 120 steers). Cattle were blocked by weight and randomly assigned to 4 pens/treatment/yr. Treatments were 1) control heifers [no growth promoters (GP)], 2) TBA implanted heifers, 3) MGA heifers, 4) control steers (no GP), and 5) TBA implanted steers (yrs 1 to 4), and included a sixth treatment 6) TBA implanted + RAC steers for yrs 3 and 4. Cattle were fed for a similar duration (262 d ± 8) in all treatments. TBA increased the DMI of steers and heifers (P < 0.001) compared to controls (9.52 vs. 8.73 kg DM/d). MGA did not affect DMI (P = 0.41); however, TBA+RAC steers had grater DMI (P = 0.02; 9.91 vs. 9.58 kg DM/d) compared to TBA steers. Compared to controls, TBA heifers had greater ADG (P < 0.001; 1.45 vs. 1.29 kg), G:F (P < 0.001; 0.157 vs. 0.149), and carcass weight (P < 0.001; 390 vs. 360 kg). TBA also increased steers ADG (P < 0.001; 1.70 vs. 1.41 kg), G:F (P < 0.001; 0.174 vs. 0.161), and carcass weight (P < 0.001; 425 vs. 389 kg). Compared to control heifers, MGA increased (P < 0.01) ADG, G:F, and carcass weight by 8.1%, 7.0%, and 4.7%, respectively. The ADG and carcass weight of TBA+RAC steers increased (P < 0.01) by 5.6% and 3.2%, respectively with no effect (P = 0.87) on G:F compared to TBA steers. This 4-yr study demonstrates the consistent positive impact of conventional growth-enhancing technologies.
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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".