Microbial Screening of Urban Stormwater and Constructed Wetlands Using Passive Sampling and TaqMan Array Cards
Bibliographic record
Abstract
This study evaluates the use of passive samplers containing passive materials and qPCR array cards to understand the microbial quality of stormwater and constructed wetlands. Passive samplers were deployed at the inlet and outlet of a stormwater-constructed wetland alongside paired water sample collection, all of which indicated high fecal contamination and human wastewater inputs. Passive materials demonstrated good performance for frequently detected enteric bacteria (e.g., Campylobacter spp.) and surrogate viruses (e.g., CrAssphage), but showed mixed performance for protozoa (e.g., Cryptosporidium spp.). Logistic regression indicated a significant interactive effect between material and location ( p < 0.01), but further analysis indicated that location was likely a proxy for turbidity, significantly different between the inlet and outlet ( p < 0.01). Some passive materials performed better at the inlet, which had a median turbidity of 20.6 NTU (e.g., cotton-based materials for Campylobacter spp.), while others were better at the outlet, with a median turbidity of 90.15 NTU (e.g., electronegative membrane for Campylobacter spp. and crAssphage, swab for crAssphage, and gauze fo r Cryptosporidium spp.). Passive sampling is a promising approach for continuously sampling urban stormwater to monitor and manage pathogen risks. Further research should compare composite samples and passive materials to evaluate target attachments during rainfall events.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".