Non‐targeted analyses of organic compounds in urban wastewater
Bibliographic record
Abstract
A large number of organic pollutants that cause damage to the ecosystem and threaten human health are transported to wastewater treatment plants (WWTPs). The problems regarding water pollution in Latin America have been well documented, and there is no evidence of substantive efforts to change the situation. In the present work, two methods to study wastewater samples are employed: non-targeted 1D ((13)C and (1)H) and 2D NMR spectroscopic analysis to characterize the largest possible number of compounds from urban wastewater and analysis by HPLC-(UV/MS)-SPE-ASS-NMR to detect non-specific recalcitrant organic compounds in treated wastewater without the use of common standards. The set of data is composed of several compounds with the concentration ranging considerably with treatment and seasonality. An anomalous discharge, the influence of stormwater on the wastewater composition and the presence of recalcitrant compounds (linear alkylbenzene sulfonate surfactant homologs) in the effluent were further identified. The seasonal variations and abnormality in the composition of organic compounds in sewage indicated that the procedure that was employed can be useful in the identification of the pollution source and to enhance the effectiveness of WWTPs in designing preventive action to protect the equipment and preserve the environment.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".