A survey to describe current feeder calf health and well-being program recommendations made by feedlot veterinary consultants in the United States and Canada
Bibliographic record
Abstract
Consulting veterinarians (CV; n=23) representing 11,295,000 head of 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 preventative medicine recommendation questions.The CV visited their feedyards 1.7 times per month.All CV train employees on cattle handling and pen riding while only 13% of CV speak Spanish.All CV recommend IBR and BVD vaccination for high-risk (HR) calves at processing.Other vaccines were not recommended as frequently by CV.Autogenous bacterins were recommended by 39.1% CV for HR cattle.Metaphylaxis and feed-grade antibiotics were recommended by 95% and 52% of CV, respectively, for HR calves.Banding was more frequently recommended than surgical castration as calf body weight increased.The CV recommended starting HR calves in smaller pens (103 hd/pen) and allowing 13 inches/hd of bunk space.The CV indicated feedlots need to employ one feedlot doctor per 7,083 hd of HR calves and one pen rider per 2,739 hd of HR calves.Ancillary therapy for treating respiratory disease was recommended by 47.8% of CV.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 in predicting feedlot morbidity while metaphylactic antibiotic, therapy antibiotic and brand of vaccine were least important.This survey has provided valuable insight into feeder cattle health recommendations by CV and points to needed research areas.
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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 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".