Investigating the Influence of Some EnvironmentalFactors on the Stability of Paracetamol,Naproxen, and Diclofenac in SimulatedNatural Conditions
Bibliographic record
Abstract
Many factors influence the persistence of traces of pharmaceuticals in aqueous environments. Most important are the intensity of light and the presence of inorganic ions as well as organic matter. We studied the impact of some environmental factors (humic acids, NO3- and NO2- ions, solar light intensity, and ambient pH) on the stability and kinetics of photo-reactions of paracetamol, naproxen, and diclofenac. It was stated that paracetamol was photoresistant, while naproxen and diclofenac were photoliable. An addition of NO3- and NO2- ions or humic acid strongly inhibited photodecomposition of paracetamol. Their presence in the solution of naproxen slowed down its decomposition, while in the case of diclofenac their influence on the kinetics of the photoreaction was neglected. The effect of the presence of the natural matrix on the photoreaction of the studied pharmaceuticals was checked. The influence of certified reference material – a water sample from the Grand River (Canada) and a sample of treated municipal waste – was examined. We observed that the matrix created by treated municipal waste acted as a photo-sensitizer. Its presence in solution accelerated the photodecomposition of all studied compounds.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".