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Record W1622922851 · doi:10.3233/ip-140330

Geomatics and Smart City: A transversal contribution to the Smart City development

2014· article· en· W1622922851 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

VenueInformation Polity · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicHuman Mobility and Location-Based Analysis
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsGeomaticsSmart cityTransversal (combinatorics)Architectural engineeringGeographyEnvironmental planningComputer scienceEngineeringCartographyComputer securityInternet of ThingsMathematics

Abstract

fetched live from OpenAlex

Information and Communication Technologies (ICTs) revolutionize the ways different urban actors communicate and interact. Geographic information technologies are of key importance too for the deployment and implementation of ICTs in the Smart City, because of the central role they may p lay as decision-making support tools. Indeed, they give quick access to different layers of information that may be combined and integrated to facilitate analysis of a situation and make the best decisions. However, such a central role is rarely acknowledged. In this paper, we propose to define the extent of the concept of Smart Cities and some of the distinctive features that it should display to support its sustainability. We will then propose an overview of the current and forthcoming developments in the geospatial domain to illustrate the opportunities that may arise for Smart City initiatives. The paper will also discuss the issues and challenges that need to be addressed when considering these emerging geomatics-driven solutions in the context of Smart City.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.656
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.015
GPT teacher head0.269
Teacher spread0.253 · 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