Sampling in the Great Lakes for pharmaceuticals, personal care products, and endocrine-disrupting substances using the passive polar organic chemical integrative sampler
Bibliographic record
Abstract
The passive polar organic chemical integrative sampler in the pharmaceutical configuration (i.e., pharmaceutical-POCIS) was calibrated for sampling at water temperatures of 5, 15 and 25 degrees C to determine the influence of temperature on chemical-specific sampling rates (R(S)), thus providing more robust estimates of the time-weighted average concentrations of pharmaceuticals and personal care products (PPCPs) and endocrine-disrupting substances (EDS) in surface water. The effect of water temperature and flow on the R(S) of these analytes was evaluated in the laboratory with a static system. The loss of the test compounds from water by uptake into POCIS was linear over an 8-d period, and these experimental data yielded R(S) values in the range of 0.07 to 2.46 L/d across the temperature range for the 30 compounds tested. Water temperature and flow influenced POCIS uptake rates, but these effects were relatively small, which is consistent with the theory for uptake into POCIS samplers. Therefore, under a narrow range of water temperatures and flows, it may not be necessary to adjust the R(S) for POCIS. Except for acidic drugs and sulfonamide antibiotics, R(S) values were positively correlated with octanol-water partition coefficients (log K(OW)) of the test compounds. A linear relationship was also observed between R(S) and chromatographic retention times on a C18 reversed-phase column. These observations may provide a rapid method for estimating the R(S) of additional chemicals in the POCIS. The application of the R(S) to POCIS deployed for one month in Lake Ontario, Canada, during the summers of 2006 and 2008 yielded estimates of PPCP and EDS concentrations that are consistent with conventional concentration measurements of these compounds in Lake Ontario surface 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".