Passive sampling of microbes in various water sources: A systematic review
Bibliographic record
Abstract
• Membrane filters are effective for detection and quantification of viruses in wastewater • Gauze passive samplers coupled with culturing is effective for detection of bacteria • Studies on passive sampling of protozoa in any water source are needed • Passive sampling papers in groundwater and seawater are lacking • Quantification and accumulation studies are needed Traditional methods for monitoring pathogens in environmental waters have numerous drawbacks. Sampling approaches that are low-cost and time efficient that can capture temporal variation in microbial contamination are needed. Passive sampling of aquatic environments has shown promise as an alternative water monitoring technique for waterborne pathogens and microbial contaminants. The present systematic review aimed to compile and synthesize existing literature on the use of passive samplers for the monitoring of microbes in different water sources and identify research gaps. The review summarizes current knowledge on materials used for detection, deployment durations, analytical methods, quantification as well as benefits and limitations of passive sampling. This review found that electronegative nitrocellulose membrane filters are effective for both detection and quantification of viruses in wastewater, while gauze passive samplers have been effective for detecting bacterial targets in wastewater. There is a large knowledge gap in the use of passive samplers in a quantitative manner, especially for the back-calculation of water-column microbial concentrations or for correlation to outcomes of interest (e.g. prevalence rates). Further, there is very limited attensstion paid to the use of membrane filters for the monitoring of bacteria in any water source as well as a lack of studies utilizing passive sampling approaches for protozoa.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".