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Record W2032327513 · doi:10.1109/icin.2015.7073815

Smart city architecture for community level services through the internet of things

2015· article· en· W2032327513 on OpenAlex

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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicIoT and Edge/Fog Computing
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsSmart cityPopulationArchitectureTraffic congestionService (business)The InternetService providerInternet of ThingsComputer scienceBusinessTelecommunicationsComputer securityTransport engineeringWorld Wide WebEngineeringGeographyMarketing

Abstract

fetched live from OpenAlex

Today, more than half of the world's population spend their lives in cities, and this number will jump to 70 percent by 2050. Increasing population density in urban environments demands adequate provision of services and infrastructure. This explosion in city population will present major challenges including air pollution, traffic congestion, health concerns, energy and waste management. Solution to these challenges might require the integration of various Information and Communication Technologies into the artifact of the city. This paper presents an architecture for smart cities, where city management, community service providers and citizens have access to real time data which has been gathered using various sensory mechanisms in order to analyze and make decisions for future planning.

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)0.001
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: Qualitative · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.698
Threshold uncertainty score0.776

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0020.001
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 head0.110
GPT teacher head0.293
Teacher spread0.184 · 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

Quick stats

Citations103
Published2015
Admission routes1
Has abstractyes

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