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Record W2033484258 · doi:10.4236/jgis.2013.52015

A Web Service for Managing Spatial Context Dedicated to Serious Games on and for Smartphones

2013· article· en· W2033484258 on OpenAlexaff
Élodie Edoh-Alove, Frédéric Hubert, Thierry Badard

Bibliographic record

VenueJournal of Geographic Information System · 2013
Typearticle
Languageen
FieldComputer Science
TopicContext-Aware Activity Recognition Systems
Canadian institutionsUniversité LavalCentre de Géomatique du Québec
Fundersnot available
KeywordsComputer scienceSpatial contextual awarenessContext (archaeology)Augmented realityService (business)GeomaticsHuman–computer interactionMultimediaMobile deviceArchitectureWorld Wide WebUbiquitous computingContext awarenessWeb serviceGeographyArtificial intelligenceCartography

Abstract

fetched live from OpenAlex

The GeoEduc3D project aims to provide educational games for smartphones based on Geomatics and use augmented reality techniques in order to make these games more immersive. To improve the immersive and interactive aspects of those games, we focused on the exploitation of spatial context in this particular application framework (serious games, augmented reality, smart phones, and multi-users environment). Our work has thus led to the design of a solution dedicated to the management of spatial context in a multi-players environment on and for smartphones. Several contributions have been made: modeling spatial context, proposing a service-oriented architecture to manage this context, defining a Web Service Spatial Context (WSCS) and implementation of a WSCS prototype and a mobile client according to an environment exploiting FourSquare, a geo-social application.

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.012
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.003
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.003

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 head0.013
GPT teacher head0.227
Teacher spread0.214 · 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 designBench or experimental
Domainnot available
GenreMethods

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

Citations4
Published2013
Admission routes1
Has abstractyes

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