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Record W4402058428 · doi:10.1061/9780784485583.046

City of Calgary’s Long-Time Feedermain Condition Assessment Program Shows This Is Not Their First Rodeo

2024· article· en· W4402058428 on OpenAlexaboutno aff
Josh Greenberg, Leo Huang, Justin Hebner

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicTransportation Safety and Impact Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.651
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0110.000

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.

Opus teacher head0.012
GPT teacher head0.267
Teacher spread0.254 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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