Perception of consultants, feedlot owners, and packers regarding the optimal economic slaughter endpoint in feedlots: a national survey in Brazil (Part I)
Bibliographic record
Abstract
Little information exists regarding the optimal economic slaughter endpoint (OSE) for feedlot-finished cattle in Brazil. This study investigated the perceptions of Brazilian feeders regarding the optimal time for slaughter. A total of 52 interviews were conducted involving nutritionist-consultants (n = 23), feedlot owners (n = 21), and packer-owned feedlots (n = 8). The results showed that 65% of the interviewees used weight and fat cover, both estimated visually, to determine the moment for slaughter. Identifying the ideal time for slaughter was considered a challenge for respondents, and 85% of them recognized that their current slaughter endpoint identification method needed improvements. Regarding decision support systems, 58% of respondents reported they would purchase a computer program to help identify OSE, and 73% would be interested in incorporating a prototype of such a system into their feedlots. Carcass dressing (38%) and price (25%) were the main factors driving the feeder’s choice of meatpacker, followed by carcass premiums (10%). Meat quality was found to be an irrelevant criterion for Brazilian meatpackers in awarding both premiums (5%) and deductions (3%). Slaughter endpoint is determined subjectively by the Brazilian feeders, based on a visual evaluation of both weight and fatness.
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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 | 0.002 | 0.004 |
| 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".