Bibliographic record
Abstract
The vast literature on antimicrobial drug use in animals has expanded considerably recently as the antimicrobial resistance (AMR) crisis in human medicine leads to questions about all usage of antimicrobial drugs, including long-term usage in intensively managed food animals for growth promotion and disease prevention. Attention is also increasingly focusing on antimicrobial use and on bacterial resistance in companion animals, which are in intimate contact with the human population. They may share resistant bacteria with their owners, amplify resistant bacteria acquired from their owners, and act as a reservoir for human infection. Considerable effort is being made to describe the basis of AMR in bacterial pathogens of animals. Documentation of many aspects of use of antimicrobials in animals is, however, generally less developed and only a few countries can describe quantities of drugs used in animals to kg levels annually. In recent years, many national veterinary associations have produced 'prudent use guidelines' to try to improve antimicrobial drug use and decrease resistance, but the impact of guidelines is unknown. Within the evolving global movement for 'antimicrobial stewardship', there is considerable scope to improve many aspects of antimicrobial use in animals, including infection control and reduction of use, with a view to reducing resistance and its spread, and to preserving antimicrobial drugs for the future.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".