Time-dependent integrity during storage of natural surface water samples for the trace analysis of pharmaceutical products, feminizing hormones and pesticides
Bibliographic record
Abstract
Monitoring and analysis of trace contaminants such as pharmaceuticals and pesticides require the preservation of the samples before they can be quantified using the appropriate analytical methods. Our objective is to determine the sample shelf life to insure proper quantification of ultratrace contaminants. To this end, we tested the stability of a variety of pharmaceutical products including caffeine, natural steroids, and selected pesticides under refrigerated storage conditions. The analysis was performed using multi-residue methods using an on-line solid-phase extraction combined with liquid chromatography tandem mass spectrometry (SPE-LC-MS/MS) in the selected reaction monitoring mode. After 21 days of storage, no significant difference in the recoveries was observed compared to day 0 for pharmaceutical products, while for pesticides, significant losses occurred for DIA and simazine after 10 days (14% and 17% reduction respectively) and a statistically significant decrease in the recovery was noted for cyanazine (78% disappearance). However, the estrogen and progestogen steroids were unstable during storage. The disappearance rates obtained after 21 days of storage vary from 63 to 72% for the feminizing hormones. Overall, pharmaceuticals and pesticides seem to be stable for refrigerated storage for up to about 10 days (except cyanazine) and steroidal hormones can be quite sensitive to degradation and should not be stored for more than a few days.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".