Multimedia screening of contaminants of emerging concern (CECS) in coastal urban watersheds in southern California (USA)
Bibliographic record
Abstract
To examine the occurrence and fate of contaminants of emerging concern (CECs) and inform future monitoring of CECs in coastal urban waterways, water, sediment, and fish tissue samples were collected and analyzed for a broad suite of pharmaceuticals and personal care products (PPCPs), commercial and/or household chemicals, current use pesticides, and hormones in an effluent-dominated river and multiple embayments in southern California (USA). In the Santa Clara River, which receives treated wastewater from several facilities, aqueous phase CECs were detectable at stations nearest discharges from municipal wastewater treatment plants but were attenuated downstream. Sucralose and the chlorinated phosphate flame retardants tris(1-chloro-2-propyl) phosphate (TCPP), tris(1,3-dichloro-2-propyl) phosphate (TDCPP), and tris(2-chloroethyl) phosphate (TCEP) were most abundant in water, with maximum concentrations of 35 μg/L, 3.3 μg/L, 1.4 μg/L, and 0.81 μg/L, respectively. Triclocarban, an antimicrobial agent in use for decades, was more prevalent in water than triclosan or nonylphenol. Maximum concentrations of bifenthrin, permethrin, polybrominated diphenyl ethers (PBDEs), and degradates of fipronil exceeded CEC-specific monitoring trigger levels recently established for freshwater and estuarine sediments by factors of 10 to 1000, respectively. Maximum fish tissue concentrations of PBDEs varied widely (370 ng/g and 7.0 ng/g for the Santa Clara River and coastal embayments, respectively), with most species exhibiting concentrations at the lower end of this range. These results suggest that continued monitoring of pyrethroids, PBDEs, and degradates of fipronil in sediment is warranted in these systems. In contrast, aqueous pharmaceutical concentrations in the Santa Clara River were not close to exceeding current monitoring trigger levels, suggesting a lower priority for targeted monitoring in this medium. Environ Toxicol Chem 2016;35:1986-1994. © 2016 SETAC.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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 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".