Antibiotics in the Environment as one of the Barriers to Sustainable Development
Bibliographic record
Abstract
The paper has analyzed the presence of antibiotics in crude (hospital, medicine production and municipal) waste water, treated waste water, surface water and drinking water across the world. The concentrations of antibiotics in medicine production waste water reached a level of up to 900 µ/dm3; in hospital waste water, up to 124 µ/dm3; and in municipal waste water, up to 64 µ/dm3. Antibiotic concentrations in treated waste water approached 260 ng/dm3. The presence of antibiotics in surface water has also been covered. The most often identified medicines were: Ciprofloxacin, Erythromycin, Norfloxacin, Sulfamethoxazole and Trimethoprim. The maximum antibiotic concentrations in surface water are as high as up to 2 µg/dm3. In the majority of cases, identified antibiotics occurred in concentrations from several to several dozen ng/dm3, and less often in several hundred ng/dm3. The presence of antibiotics in drinking water, similarly as for waste water, was identified worldwide, e.g. in China, USA, Germany, Canada, France. Very high antibiotic concentrations were noted in Guangzhou, China, which reached a level of up to 679.7 ng/dm3 (Ciprofloxacin), but also in the USA (Triclosan) – 734 ng/dm3). In the majority of instances, antibiotics are present in water in much lower concentrations. The consequence of environmental contamination with antibiotics is the drug resistance of many bacterial strains with the resultant deaths of 25 000 people in the European Union and 700 000 people across the globe. The other effects of the presence of antibiotics in the natural environment are not fully understood yet. For example, carcinogenic, teratogenic or mutagenic effects are attributed to these contaminants.
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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 | 0.002 | 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 | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".