Toxic Groundwater Contaminants: An Overlooked Contributor to Urban Stream Syndrome?
Bibliographic record
Abstract
Screening for common groundwater contaminants was performed along eight urban stream reaches (100s-1000s of m) at approximately 25-75 cm below the streambeds. Four sites had known or suspected chlorinated-solvent plumes; otherwise no groundwater contamination was known previously. At each site, between 5 and 22 contaminants were detected at levels above guideline concentrations for the preservation of aquatic life, while several others were detected at lower levels, but which may still indicate some risk. Contaminants of greatest concern include numerous metals (Cd, Zn, Al, Cu, Cr, U), arsenic, various organics (chlorinated and petroleum), nitrate and ammonium, and chloride (road salt likely), with multiple types occurring at each site and often at the same sampling location. Substantial portions of the stream reaches (from 40 to 88% of locations sampled) possessed one or more contaminants above guidelines. These findings suggest that this diffuse and variable-composition urban groundwater contamination is a toxicity concern for all sites and over a large portion of each study reach. Synergistic toxicity, both for similar and disparate compounds, may also be important. We conclude that groundwater contaminants should be considered a genuine risk to urban stream aquatic ecosystems, specifically benthic organisms, and may contribute to urban stream syndrome.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".