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Interdependencies between Urban Transport, Water, and Solid Waste Infrastructure Systems

2023· article· en· W4365145818 on OpenAlexaff
Poornima Jayasinghe, Sybil Derrible, Lina Kattan

Bibliographic record

VenueInfrastructures · 2023
Typearticle
Languageen
FieldEngineering
TopicInfrastructure Resilience and Vulnerability Analysis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsInterdependenceResilience (materials science)Context (archaeology)UrbanizationCritical infrastructureBusinessEnvironmental planningSustainabilityRisk analysis (engineering)Computer scienceEnvironmental resource managementEnvironmental economicsEnvironmental scienceGeographyComputer securityEconomic growthEconomics

Abstract

fetched live from OpenAlex

Developing integrated, sustainable, and resilient urban systems requires consideration of the different types of interdependencies between their infrastructure systems. The degree and nature of interdependencies among infrastructure systems vary widely. This article identifies and analyzes the interdependencies between urban transport, water, and solid waste. A comprehensive review is conducted, an interdependency matrix for the three systems is developed, and the interdependencies are analyzed qualitatively. The analysis shows that the three systems are highly interdependent, indicating that an integrated approach that considers the mutual impacts, conflicts, and interactions among them at all stages of their life cycles is necessary to promote sustainability and resilience. This article also identifies opportunities for developing new integrated planning and design approaches and emphasizes the need for further research in this area to quantify infrastructure interdependencies. This is particularly important in the context of rapid urbanization and the pressure on cities to adapt to climate change.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.004
Science and technology studies0.0010.002
Scholarly communication0.0020.003
Open science0.0010.002
Research integrity0.0000.001
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 head0.006
GPT teacher head0.219
Teacher spread0.213 · 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 source (direct Gemma or distilled Codex), 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

Citations25
Published2023
Admission routes1
Has abstractyes

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