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Record W2136397510 · doi:10.1061/9780784413692.021

Risk-Based Condition Assessment and Rehabilitation Planning in Colorado Springs

2014· article· en· W2136397510 on OpenAlex
Chris Macey, Darlene Garcia, Brad Croft, James Davidson

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePipelines 2014 · 2014
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsResearch Manitoba
Fundersnot available
KeywordsPrioritizationMetropolitan areaRisk assessmentPopulationPotable waterRationalization (economics)Software deploymentRehabilitationCivil engineeringComputer scienceEngineeringEnvironmental planningEnvironmental scienceEnvironmental engineeringGeography

Abstract

fetched live from OpenAlex

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.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.280
Threshold uncertainty score0.449

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)0.0000.000

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 head0.004
GPT teacher head0.241
Teacher spread0.237 · 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