Nutritional recommendations and management practices adopted by feedlot cattle nutritionists: the 2016 Brazilian survey
Bibliographic record
Abstract
The feedlot industry in Brazil is still evolving, and some nutritional management recommendations adopted by nutritionists changes from year to year. The main objective of this survey was to provide a snapshot of current nutritional management practices adopted in Brazilian feedlots. The 33 nutritionists surveyed were responsible for approximately 4 228 254 animals. Corn remained as the primary source of grain used in feedlot diets by the participants, whereas fine grinding was the primary grain processing method. Corn silage was the primary roughage source indicated by nutritionists, and for the first time, physically effective neutral detergent fiber was the preferred fiber analysis method. The average dietary fat recommended was 50 g kg−1 of dry matter, which is about 10% higher than values reported in previous surveys. The use of truck-mounted mixers increased, which may have increased the percentage of feedlots using programmed feed delivery per pen, allowing the increase of energy content of finishing diets. Feedlots did not increase their capacity and nutritionists reported an improvement in feeding management. Results reported in the current study provide a baseline that can be used to improve practices and aid in the development of feedlot industry in Brazil and similar tropical climates.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".