Multicompartment Examination of Micropollutant Partitioning in Replicate Artificial Streams Highlights the Limitations of Assessing Water Matrices Alone
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 it