Sensor Placement Optimization for Water Quality Model Calibration
Bibliographic record
Abstract
Several sophisticated methods have been developed for water quality (WQ) sensor placement in water distribution system analysis, but most are geared toward mitigating water security concerns, including but not limited to contaminant detection, chemical intrusion, or terroristic attacks. The WQ sensor or logger placement has been less concerned for the water quality monitoring or field data collection to conduct WQ model calibration. The sensor locations are conventionally determined in an ad hoc manner, based on geographic coverage, pipe diameter, pipe material, distance to the source, and accessibility. This paper presents a new methodology for helping engineers to identify the near optimal locations of WQ sensors for WQ model calibration. The approach maximizes the sensory network efficiency and the coverage of the pipes due to wall reaction coefficient adjustments that are the primary model parameters for WQ model calibration. This new method allows us to collect good data to calibrate a WQ model.
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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".