Assessing Passive Sampling for the Monitoring of <i>E. coli</i> and <i>Cryptosporidium</i> spp. in Environmental Waters
Bibliographic record
Abstract
Passive sampling has shown promise as an alternative approach for monitoring of pathogens in aquatic matrices. We conducted two controlled experiments to compare the efficacy of membrane passive sampling to composite sampling in both wastewater and surface water for the detection of Escherichia coli and Cryptosporidium . We also investigated the relative uptake of E. coli and Cryptosporidium onto membrane passive samplers over time. Both sampling methods returned positive detections of E. coli at all deployment times (4, 8, 24, 48, 72, and 96 h) in both water matrices. Passive sampling for Cryptosporidium showed similar detection rates as composite samples in surface water (31% passive; 41% composite) and wastewater (76% passive; 86% composite). We found significant linear uptake of E. coli onto passive samplers up to 96 h in surface water ( R 2 = 0.932; p = 0.002). In wastewater, maximum passive sampler uptake of E. coli was reached after 24 h. For Cryptosporidium, linear uptake was observed up to 96 h for both surface water ( R 2 = 0.805; p = 0.015) and wastewater ( R 2 = 0.877; p = 0.006). Our results support that membrane passive samplers may be used for the detection of Cryptosporidium and E. coli in surface waters for up to 96 h.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".