Survey to describe current feeder cattle health and well-being program recommendations made by Feedlot Veterinary Consultants in the United States and Canada
Bibliographic record
Abstract
Feedlot veterinarians (n=23) representing 11,295,000 cattle on feed in the United States and Canada participated in a beef cattle health and well-being recommendation survey. Veterinarians were directed to an online survey to answer feeder cattle husbandry, health, and preventive medicine recommendation questions. The feedlot veterinarians visited feedyards in their practice 1.7 times per month. All survey participants train feedlot employees on cattle handling and pen riding, while only 13% of respondents speak Spanish. All recommend infectious bovine rhinotracheitis and bovine viral diarrhea vaccination for high-risk (HR), cattle at processing; other vaccines were not recommended as frequently. Autogenous bacterins were recommended by 39.1% of feedlot veterinarians for HR cattle, while metaphylaxis and feed-grade antibiotics were recommended by 95% and 52% of respondents, respectively, for HR cattle. Banding was more frequently recommended than surgical castration as calf body weight increased. Feedlot veterinarians recommended starting HR cattle in smaller pens (103 head per pen) and allowing 13 inches (33 cm) per head of bunk space. They further recommend that feedlots employ one feedlot doctor per 7,083 head of HR cattle, and one pen rider per 2,739 head of HR cattle. Ancillary therapy for treating respiratory disease was recommended by 47.8% of veterinarians surveyed; vitamin C was recommended (30.4%) twice as often as any other ancillary therapy. Cattle health risk on arrival, weather patterns, and labor availability were most important factors for predicting feedlot morbidity, while metaphylactic antibiotic, therapy antibiotic, and brand of vaccine were least important. This survey provides valuable insight into feeder cattle health recommendations by feedlot veterinarians in the US and Canada, and provides direction for future research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".