Perception of consultants, feedlot owners, and packers regarding management and marketing decisions on feedlots: a national survey in Brazil (Part II)
Bibliographic record
Abstract
Research interviews with agribusiness professionals are carried out in several countries for updating and developing technologies. This study aimed to investigate the perception of Brazilian feeders regarding management and marketing tools used in the feedlot industry. Interviews were conducted with groups: nutritionist-consultants (n = 23), feedlot owners (n = 21), and packer-owned feedlots (n = 8). Roughly 58% of the interviewees worked with two cycles of animals per year. Roughly 80% of animals on feedlots were males, with 73% of the respondents having fed only intact males and 75% of the animals were Nellore breed. Among the criteria used for pen formation, weight was the most common (75%). The use of computational tools for feedlot management (71%) and diet formulation (69%) were found to be common, although interviewees did not use any software to characterize feeder animals. In 44% of the respondent feedlots, animals that reached the desired weight and degree of finish were removed for slaughter, whereas the unfinished animals remained in the same pen. We found that a need, therefore, exists to develop efficient strategies for forming homogeneous pens upon animal entry onto feedlots, and maintaining homogenous pens upon the exit of animals for slaughter.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".