City of Calgary’s Long-Time Feedermain Condition Assessment Program Shows This Is Not Their First Rodeo
Bibliographic record
Abstract
The City of Calgary has long taken a proactive approach to the management of their large diameter feedermain network. This includes an advanced condition assessment strategy that combines an inspection program and risk analyses to develop a better understanding of the safety and reliability of the network. The program began in 2004, after the 1,200 mm (48-in.) McKnight Feedermain catastrophically failed and released 20 mL (5 mg) of water, flooding a roadway, and disrupting service to over 100,000 customers. An investigation determined that the failure occurred on a pipe section with coating that was eaten away in sulfate rich soils, allowing water to seep in and corrode the steel structure of the pipe. Over the years, successful inspections have allowed the City to proactively repair damaged pipes. This presentation will provide an update on Calgary’s long-term strategy to pipeline management, including historical real-time structural monitoring results of the McKnight Feedermain and proactive leak detection success stories on their large diameter feedermain network. Inspection methods and technologies utilized, including damaged pipes that were identified and repaired prior to failure will be highlighted, along with challenges and recent improvements to the assessment process.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.036 | 0.004 |
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".