Antibiotics in Drinking Water in Shanghai and Their Contribution to Antibiotic Exposure of School Children
Bibliographic record
Abstract
A variety of antibiotics have been found in aquatic environments, but antibiotics in drinking water and their contribution to antibiotic exposure in human are not well-explored. For this, representative drinking water samples and 530 urine samples from schoolchildren were selected in Shanghai, and 21 common antibiotics (five macrolides, two β-lactams, three tetracyclines, four fluoquinolones, four sulfonamides, and three phenicols) were measured in water samples and urines by isotope dilution two-dimensional ultraperformance liquid chromatography coupled with high-resolution quadrupole time-of-flight mass spectrometry. Drinking water included 46 terminal tap water samples from different spots in the distribution system of the city, 45 bottled water samples from 14 common brands, and eight barreled water samples of different brands. Of 21 antibiotics, only florfenicol and thiamphenicol were found in tap water, with the median concentrations of 0.0089 ng/mL and 0.0064 ng/mL, respectively; only florfenicol was found in three bottled water samples from a same brand, with the concentrations ranging from 0.00060 to 0.0010 ng/mL; no antibiotics were found in barreled water. In contrast, besides florfenicol and thiamphenicol, an additional 17 antibiotics were detected in urine samples, and the total daily exposure doses and detection frequencies of florfenicol and thiamphenicol based on urine samples were significantly and substantially higher than their predicted daily exposure doses and detection frequencies from drinking water by Monte Carlo Simulation. These data indicated that drinking water was contaminated by some antibiotics in Shanghai, but played a limited role in antibiotic exposure of children.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".