The Heartland of Alberta’s Approach to Leak Detection
Bibliographic record
Abstract
In 2022, the Town of High River, Alberta, started a leak detection program on a series of critical ductile iron (200-, 350-, and 400-mm diameter) and PVC (300-mm) raw watermains and potable distribution mains as well as a steel river crossing. A 2013 flood put much of High River under several feet of water, leading to extensive damage and requiring hundreds of millions of dollars spent on flood mitigation and disaster relief over the past decade. Flooding has the potential to damage a variety of critical infrastructure, including pump stations and buried pipelines. As part of its “build it back better approach,” High River teamed with Pure Technologies and identified and repaired two leaks, reducing lost revenue and ensuring reliability of the system for their growing population. This project will highlight non-disruptive condition assessment approaches for small diameter pipelines managed by small utilities.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".