Recreational water monitoring: Nanofluidic qRT‐PCR chip for assessing beach water safety
Bibliographic record
Abstract
Abstract Improved recreational water monitoring using rapid molecular genetic methods would decrease both public health risks and unnecessary beach closures. Hence, we developed a novel nanofluidic quantitative real‐time PCR (qPCR) in the OpenArray platform to (a) detect and quantify fecal indicator bacteria (FIBs; N = 2), (b) identify contaminant sources (microbial source tracking (MST); N = 7), and (c) detect human virulent pathogens (virulence gene markers; N = 15). Our water monitoring OpenArray plate reliably detects as few as two template copies/hole (OpenArray ® well), with some marker sensitivities as low as single‐copy detection. The OpenArray plate showed high target sequence specificity and returned expected patterns of contaminants for fecal and sewage samples. We found Canada geese and seagulls were the leading causes of contamination at beaches that tested positive for coliforms. When we incorporated robotic DNA extraction, we were able to process samples from water to FIBs, MST, and waterborne pathogen detection and quantification within four hours. Our monitoring TaqMan qPCR assays in the OpenArray platform is uniquely valuable for regulatory agencies charged with beach water safety as well as for researchers interested in human health implications of aquatic microbial community structure.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".