Assessment of water main break data for ASSET MANAGEMENT
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
Data regarding water main breaks are widely considered important for effective water distribution asset management. This article presents the results of a survey administered to small and medium‐sized utilities to evaluate the state of data collection practices for water main breaks in the United States and Canada. The survey included questions about the amount and type of data collected by water utilities, the utilities' level of comfort with the amount of data collected, and the availability of data elsewhere within the utility. The survey results show that the amount of data collected can be classified by the degree of data richness and defined as either an expanded, intermediate, limited, or minimal data set. Analysis of the results suggests that utilities can implement practices to increase the amount of data they collect and increase the effectiveness of their data collection and processing. The results also suggest that utilities can improve their data sets by considering additional sources of data for water main breaks.
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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