Current Technologies For On-Line Monitoring of Drinking Water Distribution Systems
Bibliographic record
Abstract
Increased risks of drinking water contamination are incentives for implementation of improved monitoring approaches for pathogens in drinking water distribution systems. However, indirect measurements such as pH, and turbidity to detect pathogens do not guarantee the safety of drinking water because pathogens, and especially protozoa and viruses, are not well correlated to these indirect measures. Routine methods for culturing indicator micro-organisms need considerable time and hence do not provide significant value for real-time monitoring efforts. Review of currently available technologies (spectral fluorescence laser technology, image processing technology, multi-angle light scattering laser technology, on-line chemical characterization system, decision support monitoring system, and rapid response tests for total micro-organisms and toxicity) for real-time and/or rapid pathogen detection, demonstrates there is no single instrument currently available. The most effective approach currently available is demonstrated to involve the identification of the presence of a pathogen, and the instigation of collection of a sample for more detailed analyses for confirmation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".