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Record W2766011257 · doi:10.1061/9780784481219.021

Identifying and Addressing Gaps for Resilient Infrastructure: A Case of Combined Stormwater Systems

2017· article· en· W2766011257 on OpenAlexaffabout
Jyoti Kumari Upadhyaya, Mirandi Lynn McDonald, Nihar Biswas, Edwin Tam

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicInfrastructure Resilience and Vulnerability Analysis
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsSustainabilityStormwater managementStormwaterAsset (computer security)Asset managementCritical infrastructureRisk analysis (engineering)Environmental planningBusinessResilience (materials science)SurvivabilityComputer scienceEnvironmental resource managementSurface runoffEnvironmental scienceComputer security

Abstract

fetched live from OpenAlex

This paper presents the preliminary findings of an ongoing study to evaluate the combined sewer stormwater infrastructure of a medium sized municipality in Ontario, Canada, using the previously developed and tested functionality-survivability-sustainability (FSS) framework. Many cities in Canada have implemented asset management programs to improve their infrastructure renewal decisions and to better direct their resources, but conventional asset evaluations do not incorporate the emerging issues of resiliency and sustainability facing modern infrastructure. These omissions create a gap in understanding infrastructure needs and impacts in a comprehensive manner. The FSS defines resource minimization, public health, and change management (RPC) as the main criteria for evaluating the system. A detailed guide has been developed to score and evaluate various indicators based on multicriteria assessment. By conducting an integrated assessment of the combined stormwater system in the study area, this paper identifies opportunities for improving infrastructure operation, resiliency, and sustainability.

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 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 score0.276
Threshold uncertainty score0.479

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 head0.023
GPT teacher head0.288
Teacher spread0.265 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2017
Admission routes2
Has abstractyes

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