Multiresidue determination of fluoroquinolone, sulfonamide, trimethoprim, and chloramphenicol antibiotics in urban waters in China
Bibliographic record
Abstract
A feasible method has been optimized to simultaneously determine multiclass antibiotic residues, including sulfonamides, fluoroquinolones, trimethoprim, and chloramphenicol in urban riverine water and wastewater by off-line solid phase extraction and high-performance liquid chromatography coupled with a diode-array ultraviolet detector and a fluorescence detector. Internal standard and standard addition methods were used in combination to identify and quantify these antibiotics to compensate for the matrix interference. The method quantification limits (MQLs) were determined to be 0.035 to 0.100 microg/L and 0.100 to 0.300 microg/L for the riverine water and wastewater, respectively. Recoveries of the investigated antibiotics ranged from 63 to 126%. Sulfamethoxazole was the most frequently detected antibiotic residue in Guangzhou section of the Major Pearl River, South China, with a maximum level of 0.510 microg/L. Fluoroquinolone antibiotics were relatively less detected with a maximum level of 0.459 microg/L. The maximum concentration of sulfamethoxazole reached 5.597 microg/L in the raw wastewater from a large-scale sewage treatment plant in Guangzhou city. Around 30% of sulfamethoxazole might survive the primary clarification and biotreatment processes in the sewage treatment plant. None of the investigated antibiotics have been found above MQLs in the final effluent after chlorine disinfection.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".