Occurrence of Pharmaceutical Contaminants and Screening of Treatment Alternatives for Southeastern Louisiana
Bibliographic record
Abstract
Recent studies conducted in Germany, Switzerland, Denmark, Brazil, Canada, the United States, and elsewhere indicate that low-level concentrations of pharmaceuticals and personal-care products (PPCPs) and their metabolites may be widespread contaminants in our aquatic environment. The persistence of pharmaceutical contaminants has been attributed to (1) human consumption of drugs and subsequent discharges from sewage treatment plants, and (2) veterinary use of drugs and nonpoint discharges from agricultural runoff. Contamination of water resources by these compounds, particularly endocrine disrupting chemicals (EDCs), is emerging as an international environmental concern. The long-term effects of continuous, low-level exposure to PPCPs is not well understood. Preliminary data for raw water samples collected from the Mississippi River and Lake Pontchartrain, Louisiana, are summarized. Three PPCP compounds (clofibric acid, naproxen, and estrone) were analyzed using solid-phase extraction, derivatization, and GC/MS. Batch experiments also were conducted to determine equilibrium capacity of activated carbon for clofibric acid. Preliminary results indicate the occurrence of the selected PPCP contaminants in raw water samples at or near method-detection limits. For batch equilibrium experiments, preliminary results indicate that activated carbon potentially can be used to remove clofibric acid from water. More research is needed to develop rapid and reliable methods for PPCP analysis and to determine the effectiveness of treatment processes for removal of PPCP contaminants in water.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".