Bibliographic record
Abstract
Since antibiotics have been added to animal feed for decades, foodanimals and their wastes constitute a reservoir of antibiotic-resistant bacteria. Atthe Swine Complex of McGill University, the addition of antibiotics to swine feedfor subtherapeutic applications has been discontinued since January 2007. Theobjective of this work was to assess the prevalence and short-term evolution ofantibiotic resistance among bacterial populations in swine production 2.5 yearsafter this discontinuation. Feces from ten healthy pigs (6 males and 4 females)born at the Swine Complex of McGill from the same sow and administered feedwithout antibiotics were sampled during suckling, weanling, growing andfinishing. The percentage of chlortetracycline-resistant anaerobic bacterialpopulations (TetR) was higher than that of tylosin-resistant anaerobic bacterialpopulations (TylR) at weanling, growing and finishing, with generally largerdifferences in males than in females. At the finishing stage, i.e. prior to thetransportation of animals to the slaughterhouse, resistant populations variedbetween 3.1x10^6 and 2.5x10^9 CFU g^-1. In all pigs, tet(L), tet(O) and erm(B) weredetected by PCR at suckling and weanling, whereas only tet(O) was detected atgrowing and finishing. Quantification of tet(O) by real-time PCR showed that atsuckling, the abundance of this gene was 18 times higher in females than inmales, was similar between the two genders at weanling and growing, andreached 5.1x10^5 and 5.6x10^5 copies of tet(O)/g of total DNA in the feces ofmales and females, respectively, at finishing. In this study, the high abundanceand proportion of antibiotic-resistant populations, as well as the occurrence ofresistance genes within these populations despite the discontinuation of antibioticaddition to feeds imply either that more time would be required for antibioticresistance to decrease to lower levels, and/or that factors such as the presence ofmetals in feed impose a selective pressure that maintains antibiotic resistancegenes among these bacterial populations.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".