Emerging Contaminants in Streams of Doce River Watershed, Minas Gerais, Brazil
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it