Current feedlot cattle health and well-being program recommendations in the United States and Canada
Bibliographic record
Abstract
Veterinary consultants routinely give recommendations to feedlot employees and managers on all areas of cattle health and well-being. Recommendations are made based on veterinarians’ field experience and review of peer-reviewed literature. However, there is little data available about how the literature is merged with field experience and the actual recommendations given by consulting veterinarians to feedlot employees and managers. A survey conducted by Vasconcelos and Galyean (2007) reported baseline recommendations of select feedlot nutritionists in the United States. This survey is to be repeated every 4 to 5 years, as changes in recommendations can be useful in determining areas in nutritional practices that warrant further research. A similar study was conducted for feedlot veterinary recommendations in 2009 to establish a baseline for recommendations of feedlot veterinary consultants in the United States and Canada. The objective of the current survey was to report specific recommendations currently made by feedlot consulting veterinarians and to compare the current recommended practices to those recommended in the survey conducted 5 years ago.
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.
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 | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".