Emerging Contaminants in Streams of Doce River Watershed, Minas Gerais, Brazil
Bibliographic record
Abstract
This study investigated the occurrence and risk assessment of ten pharmaceutical products and two herbicides in the water of rivers from the Doce river watershed (Brazil). Of the 12 chemicals studied, ten (acyclovir, amoxicillin, azithromycin, ciprofloxacin, enrofloxacin, fluoxetine, erythromycin, sulfadiazine, sulfamethoxazole, glyphosate and aminomethylphosphonic acid) had a 100% detection rate. In general, total concentrations of all target drugs ranged from 4.6 to 14.5 μg L −1 , with fluoroquinolones and sulfonamides being the most representative classes of pharmaceutical products. Herbicides were found at concentrations at least ten times higher than those of the individual pharmaceutical products and represented the major class of contaminants in the samples. Most of the contaminants studied were above concentrations that pose an ecotoxicological risk to aquatic biota. Urban wastewater must be the main source of contaminants in waterbodies. Our results show that, in addition to the study of metal in water (currently being conducted after the Fundão dam breach), there is an urgent need to monitor emerging contaminant in waters from Doce river watershed rivers, as some chemicals pose environmental risks to aquatic life and humans due to the use of surface water for drinking and domestic purposes by the local population. Special attention should be given to glyphosate, aminomethylphosaphonic acid, and to ciprofloxacin and enrofloxacin (whose concentrations are above predicted levels that induce resistance selection).
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".