Rapid Detection of Bacteria in a Water Distribution System
Bibliographic record
Abstract
A water distribution system may be contaminated by bacteria through crossconnections, by intrusion of soil water after a pressure loss due to a power blackout, or by the intentional contamination of the system in a bio-terrrorism event.The problem a utility faces is to detect it in a timely fashion, and to take immediate action to correct the problem and at least warn consumers.Currently approved microbiological methods are all culture based.That is, after filtration the water sample is placed on an agar plate and then incubated at varying temperatures for one to seven days.A determination of the indicator bacteria E. coli takes 24 hours, and the most sensitive test for total bacteria is an HPC count of the colonies growing on R2A agar for three to seven days.These tests are clearly not useful for protection of the consumers in a timely fashion.We have worked on a rapid test method whereby we capture the bacteria from a water sample on a 0.45 micron filter and then lyse the bacteria.The released adenosine triphosphate (ATP) from the bacteria reacts with added luciferin/luciferase and then light emission takes place that can be measured with a luminometer.The whole procedure takes less than five minutes.The amount of ATP is proportional to the number of bacteria.To test if a sewage intrusion can be detected, experiments have been conducted in beakers where a small quantity of sewage (secondary effluent of the Ann Arbor Wastewater Treatment Plant (AAWWTP) was added to water samples, and the bacterial counts were determined by ATP analysis and the
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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 | 0.001 |
| 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".