Non-Target Screening Of Organic Micropollutants In Durgam Cheruvu Lake, India
Bibliographic record
Abstract
The main objective of this study is to evaluate the water quality of Durgam Cheruvu Lake, Hyderabad, India by comprehensively analysing organic micropollutants.Samples were collected from three different sites in the lake and nontargeted screening was performed using liquid chromatography-quadrupole time-of-flight (LC-QTOF).A total of 183 compounds were detected in all samples.This includes pharmaceuticals, herbicides, fungicides, pesticides, hormones, steroids, UV filters, plasticizers, cyanotoxins, and metabolites.In all samples, pharmaceuticals accounted for approximately 50%, herbicides 8%, and metabolites 9%.The high abundance values were observed for 17 -Dihydroequilin, Avobenzone, Sibutramine, Butachlor, Napropamide, and Estriol at all the sampling locations.Estriol and 17 -Dihydroequilin are classified as the largest endocrine disruptors among many micropollutants.Eutrophication-related cyanotoxins including Microcystin-LR and Anatoxin-A have been identified in the lake.Additionally, the urine metabolites of Clarithromycin, Flunitrazepam, and other transformed metabolites of cocaine-d3 and Amitriptyline were discovered.Overall, veterinary medications, narcotic pharmaceuticals, pain killers, anti-psychotic, anti-depressant, and anti-obesity drugs were found to be the most prevalent components in the lake samples, indicating the discharge of domestic and industrial wastewater into the lake.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".