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Municipal Infrastructure Prioritization based on Consequence-Based Decision-Making Framework

2020· article· en· W3125017991 on OpenAlexaff
Golam Kabir, Abdullah Alzahrany

Bibliographic record

Venue2020 International Conference on Decision Aid Sciences and Application (DASA) · 2020
Typearticle
Languageen
FieldEngineering
TopicInfrastructure Resilience and Vulnerability Analysis
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsPrioritizationRisk analysis (engineering)Expert opinionComputer scienceCausal structureManagement scienceBusinessEngineering

Abstract

fetched live from OpenAlex

The breakdown of municipal infrastructures may trigger serious effects to the society, environment, and economy. In this study, a systematic consequence-based decision-making framework is developed based on expert's judgment or opinion. The causal relationships between various factors is established based on available data, previous literature, and expert judgment. To develop the consequence-based decision-making framework, Decision Making Trial and Evaluation Laboratory (DEMATEL) method is integrated with Interpretive Structure Modeling (ISM). The results of this study indicate that a complex causal relationship exists between the consequence factors. The developed framework can be used to prioritize the municipal infrastructures for maintenance, repair, renovation, and replacement and to represent environmental, social, safety, and economic impacts.

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)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.810
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.020
GPT teacher head0.310
Teacher spread0.290 · 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

Citations3
Published2020
Admission routes1
Has abstractyes

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