Integration of Incomplete Mixing at Junctions of Water Distribution Networks in Contamination Source Identification Tools
Bibliographic record
Abstract
The water quality models used in contamination source identification (CSI) tools assume complete mixing at the junctions of drinking water distribution networks. Two extensions of the contamination status algorithm (CSA)—a CSI tool that employs water quality models in a reverse-time manner—were accordingly developed in this study, one assuming complete mixing (CSA-CMX) and the other assuming incomplete mixing (CSA-IMX) at cross-junctions. Both algorithms identified contamination sources based on the results of grab sampling at iteratively suggested locations. The performances of CSA-CMX and CSA-IMX were evaluated through laboratory experiments using three contamination identification problems: CSA-IMX identified the contamination source in all three problems, whereas CSA-CMX identified the contamination source in only one. Furthermore, the specificity (i.e., the ability to distinguish the real contamination source from other possible contamination sources) was higher for CSA-IMX than for CSA-CMX in two of the three problems. Therefore, the incomplete mixing assumption was confirmed to be a crucial factor in CSI tools.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".