A survey to describe current feeder calf health and well-being program recommendations made by feedlot veterinary consultants in the United States and Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it