Bibliographic record
Abstract
This paper reviews the present state of antimicrobial resistance (AMR) in the zoonotic bacteria Salmonella, Campylobacter jejuni and Campylobacter coli, and in Escherichia coli from chickens and turkeys. For Salmonella, the frequencies and patterns of AMR vary depending on time, region, serovar, the particular farm, layers versus broilers, and the antimicrobial agent. There is usually a higher frequency of AMR in Salmonella from turkeys compared with Salmonella from chickens. Clonal and horizontal transmission of AMR occur and there is concern about the spread of transmissible plasmids that encode extended spectrum cephalosporinases. Resistance to fluoroquinolones is generally low. For Campylobacter, resistance to tetracycline is usually at moderate to high frequency, resistance to quinolones/fluoroquinolones varies from low to high, and resistance to macrolides is usually low. There are high levels of fluoroquinolone resistance in some countries. Avian pathogenic E. coli are often highly resistant, especially to tetracycline, streptomycin, and sulfonamides. Plasmid-mediated resistance is common. High levels of resistance to ciprofloxacin have been reported from China. Commensal E. coli from poultry have similar patterns of resistance but at lower frequencies. Integron associated resistance occurs commonly in Salmonella and E. coli but has not been detected in Campylobacter.
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 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".