Risk-Based Condition Assessment and Rehabilitation Planning in Colorado Springs
Bibliographic record
Abstract
Colorado Springs, Colorado, has a population of more than 400,000 people with a metropolitan service area of more than 600,000. Colorado Springs Utilities (CSU) had identified a need to evaluate the effectiveness of its current potable water project identification and prioritization model, assess different techniques to ascertain pipe condition and predict pipe failures, and assess alternatives to open cut replacement that would mitigate the risk of water main failure at a lower cost and with reduced impact to customers. This paper provides an overview of studies carried out on the existing program and potable water system in Colorado Springs and the development of a unique risk-based framework to facilitate both condition assessment and rationalization of rehabilitation requirements for its entire potable water distribution network. It also provides both an overview of the overall risk-based approach and examples of initial condition assessment programming tools, techniques, and results. Phase 1 of the program was completed in 2012/2013 and Phase 2, which includes the deployment of field-based condition assessment activities, commenced in 2014.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".