Widespread detection of antibiotic-resistant bacteria from natural aquatic environments in southern Ontario
Bibliographic record
Abstract
To elucidate how widespread antibiotic resistance is in the surface water environment, we studied the prevalence of antibiotic resistance bacteria at four locations in southern Ontario. We found that the percentage of bacteria resistant to the antibiotic tetracycline was higher at the river site, which flows through agricultural land, and lower at the lake sites. A total of 225 colonies were selected for further testing of antibiotic disc susceptibility to eight different antibiotics to calculate the multiple antibiotic resistance (MAR) score and the antibiotic resistance index for each site. Although the isolates from the lake site outside the city displayed resistance to fewer antibiotics, their MAR scores were not significantly different from that of the lake sites adjacent to urban beaches, showing that MAR was widespread in the natural water environments tested. Isolation of colonies under selection pressure to tetracycline was found to have a significant effect on the likelihood that the isolates would contain multiple resistance traits for other antibiotics. Identification of isolates selected on tetracycline was compared with that of isolates that were sensitive to tetracycline, and the community composition was found to be distinctly different, although isolates from the genera Chryseobacterium, Pseudomonas, and Stenotrophomonas were found in both communities.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".