Animal Performance, Feeding Behaviour and Carcass Traits of Feedlot Cattle Diet Fed With Agro-Industrial By-Product as Fat Source
Bibliographic record
Abstract
In feedlot system is particularly important to reduce the cattle feeding cost without impact on the animal gain and carcass, in this sense, aimed with this study to evaluate animal performance and carcass traits of the young Nellore male (n = 40) finished with agro-industrial by-products in feedlot diet. The addition of cottonseed by-product (CSB) was based on the ether extract (EE) contents in the feedlot diet: 3, 4 and 5%; and two other reference treatments were also tested, with 3 and 5% of EE content and soybean by-product (SOB) as fat source, totalling five experimental diets. In diets with 3% EE, CSB did not alter performance, gain cost or carcass traits compared to the SOB. In diets with 5% EE, animals fed with CSB showed greater dry matter (DM) intake than animals fed with SOB (10.34 versus 8.94 kg/day), but because CSB is a cheaper ingredient than SOB, it reduced the gain cost from 1.60 to 1.35 US$/kg. The CBS used in diet with 3, 4 and 5% EE increased the daily gain (1.17, 1.38 and 1.50 kg/day) and the rumination time (225, 338 and 370 min/day, respectively). So, CSB does not change the carcass traits nor the feeding behaviour when compared to SOB. The increased of CSB concentration in the diet raised the daily gain, DM intake and rumination time, with no changes in carcass traits.
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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".