Explaining the occurrence of coliforms in distribution systems
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
According to the Total Coliform Rule, coliform bacteria constitute the main indicator used to detect microbial contamination in distribution systems. A major goal for water utilities is to prevent and control coliform occurrences and noncompliance (with respect to regulations). However, the applied solutions are sometimes successful only to a limited extent because of the variety of factors that may give rise to these occurrences. It is therefore important to identify those factors—from the structure and operation of the distribution system to the quality of the distributed water itself—that can influence the occurrence of coliform bacteria in a distribution system. The use of the identified factors makes the modeling of coliform occurrences attractive, and a number of approaches for doing so have recently been proposed. This article provides a review that includes the mechanisms of how coliform bacteria are introduced into treated and distributed drinking waters, the major factors controlling the survival and regrowth of coliforms once introduced into the system, and the modeling efforts carried out to explain or predict their occurrence.
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.
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 it