Multicompartment Examination of Micropollutant Partitioning in Replicate Artificial Streams Highlights the Limitations of Assessing Water Matrices Alone
Bibliographic record
Abstract
While numerous assessments of micropollutant exposure primarily focus on monitoring the water column, a growing body of research indicates that differences in micropollutant partitioning in other compartments require additional consideration for risk evaluation. This study investigated the partitioning of antibiotics, antiepileptics, antibacterials, and antidepressants and their metabolites in water, sediment, macroinvertebrates (gammarids), biofilm, and fish (spoonhead sculpin and longnose dace) found or exposed in replicate naturalized streams (Calgary, Alberta, Canada). All target micropollutants were detected in the water and sediment, and >5 substances were detected in the biotic matrices at concentrations between the limit of quantitation and 244 ± 16 ng/g dw . Triclosan and triclocarban (antibacterials) were frequently detected in sediments, but very rarely in the water column. The solid–water partitioning ( K d ) and organic carbon–water partitioning coefficients ( K oc ) indicate that fluoxetine, norfluoxetine, and triclosan have a stronger affinity for sediments and/or organic matter (log K d > 2.7, log K oc > 1.5). More specifically, fluoxetine was found to be up to 10× higher in sediments, biofilm, and gammarids than other substances, whereas its concentration in the water column was very low or nondetectable. Finally, bottom-dwelling fish (spoonhead sculpin) were also found to have higher concentrations of fluoxetine and its metabolite than longnose dace.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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 | 0.000 |
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".