Spatial Point Pattern Analysis for Water Mains Failures in Sanandaj, Iran
Bibliographic record
Abstract
This chapter addresses a methodology for using spatial and statistical analysis to discover the distribution of water mains failures in space. It provides a number of new ways of looking at water mains failure data and geographical relations among them. Points are used to indicate spatial occurrences of failures and their pattern. Data from Sanandaj city in Iran over a ten year period is analysed, including 395 mains failure. Point pattern analysis is applied to exploring spatial variation and identifying whether occurrences are interrelated. Measures of central tendency scores like mean center, weighted mean center and dispersion scores such as standard deviation distance and standard deviation ellipse help explain the level of dispersion in failure data. In addition, nearest neighbor analysis and quadrat analysis were performed to find the spatial pattern in the point distribution. The findings confirm that water mains failures tend to occur in cluster. This will enable more effective management of the water distribution network by ensuring that mains are rehabilitated or replaced in cluster areas.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".