Economic Impact of Antimicrobial Use in Feedlots
Bibliographic record
Abstract
In beef feedlot production, antimicrobials are used daily to control disease and improve production. The vast majority of feedlot animals receive ionophores in the feed to control coccidiosis and improve feed efficiency, and antimicrobials in the feed to reduce the incidence of liver abscesses. Other antimicrobials, such as decoquinate, chlortetracycline and sulfamethazine occasionally are used according to label recommendations to prevent or control specific disease outbreaks and/or to "aid in the maintenance of weight gains and feed efficiency in cattle during periods of stress, due to weaning, shipping or handling." Parenteral antimicrobials also are used in high-risk populations according to label recommendations to prevent, control and/or treat disease. Because increasing antimicrobial resistance in human pathogens poses a serious threat to the treatment of infectious disease in humans, use of antimicrobials in animal agriculture is under heavy scrutiny from several prominent scientists. The source of this resistance is speculated to lie with in the widespread use of antimicrobials in farm animal production. As a result, it has been suggested that the use of antimicrobials in animal agriculture be limited to therapeutic applications, which would substantially reduce the cost of production associated with antimicrobial costs. The latter theory ignores the economic benefits associated with non-therapeutic antimicrobial usage. It is imperative that these economic benefits are accurately described so that rational, informed, data-based decisions regarding the future of antimicrobial usage in food animal production can be made.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".