From bench to beach: Assessing the reliability of community-based qPCR monitoring for recreational water quality
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Quantitative polymerase chain reaction (qPCR) is increasingly used in recreational water quality monitoring, yet the temporal variability of indicator concentrations as well as the breadth of locations and biological hazards to be monitored continues to present a challenge. Participatory approaches such as community-based monitoring (CBM) are valued in environmental research but the potential for the integration of DNA-based methods has yet to be realized. This study assessed the reliability of a decentralized, community-based qPCR monitoring program for fecal indicator bacteria, Enterococcus spp. , in recreational waters. Non-expert community partners were responsible for DNA extraction and qPCR analysis of samples at a satellite laboratory; training, protocols, and materials were provided and standardized by our research team. Comparison of community partner results to those from duplicate samples analyzed by our research team following U.S. EPA Method 1611 revealed a high level of reliability, with 72.8% of community partner results indicating the same beach management decision as Method 1611. Median coefficient of variation between community partner and Method 1611 results ranged from 7.07% to 10.29%. In this study, we demonstrate the ability of non-expert community partners to independently carry out protocols and to generate reliable qPCR monitoring data for water quality indicators and the strong relationship between the results of this community-based approach and gold standard methods. As the employment of DNA-based testing expands, incorporation of these techniques into a CBM framework presents a means to advance and expand traditional monitoring and research approaches by increasing capacity, addressing gaps, fostering greater inclusivity and community engagement in monitoring and management, and improving the accessibility of environmental research.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it