Automated Targeted Sampling of Waterborne Pathogens and Microbial Source Tracking Markers Using Near-Real Time Monitoring of Microbiological Water Quality
Bibliographic record
Abstract
Waterborne pathogens are heterogeneously distributed across various spatiotemporal scales in water resources, and representative sampling is therefore crucial for accurate risk assessment. Since regulatory monitoring of microbiological water quality is usually conducted at fixed time intervals, it can miss short-term fecal contamination episodes and underestimate underlying microbial risks. In the present paper, we developed a new automated sampling methodology based on near real-time measurement of a biochemical indicator of fecal pollution. Online monitoring of β-D-glucuronidase (GLUC) activity was used to trigger an automated sampler during fecal contamination events in a drinking water supply and at an urban beach. Significant increases in protozoan parasites, microbial source tracking markers and E. coli were measured during short-term (<24 h) fecal pollution episodes, emphasizing the intermittent nature of their occurrence in water. Synchronous triggering of the automated sampler with online GLUC activity measurements further revealed a tight association between the biochemical indicator and culturable E. coli. The proposed event sampling methodology is versatile and in addition to the two triggering modes validated here, others can be designed based on specific needs and local settings. In support to regulatory monitoring schemes, it should ultimately help gathering crucial data on waterborne pathogens more efficiently during episodic fecal pollution events.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".