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Record W2112523529 · doi:10.1109/vsmm.2012.6365991

A novel approach for tourism and education through virtual Vitoria-Gasteiz in the 16<sup>th</sup> century

2012· article· en· W2112523529 on OpenAlexaboutno aff
Ainhoa Pérez-Valle, Diego Sagasti

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldArts and Humanities
TopicHistorical Art and Architecture Studies
Canadian institutionsnot available
Fundersnot available
KeywordsTourismCultural heritagePopulationQuarter (Canadian coin)Value (mathematics)Relation (database)Computer scienceLibrary scienceWorld Wide WebSociologyHumanitiesGeographyArtArchaeologyDemography

Abstract

fetched live from OpenAlex

This paper introduces new approaches to the educational and tourism domains of Vitoria-Gasteiz in the 16 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> century. Both spheres present considerable technology breakthroughs to be used in cultural heritage conservation and preservation, as well as to engage the population in cultural knowledge through serious games. The material presented in this paper concerns the transmission of cultural heritage applied mainly to the two areas mentioned above. The game is based on a virtual model of Vitoria-Gasteiz's (Spain) Old Quarter in the 16 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> century. The virtual reconstruction has been divided in two phases. The first reconstruction stage is focused on the more significant buildings of that époque, while the second is focused on a procedural software that was used for the automatic re-creation of the majority of the buildings. These paper analyzies previous studies on motivation and the value serious games have to offer for education and tourism. In relation to tourism, Virtual Reality's utility as a preservation tool derives from its potential to educate and create experiences. This allows visitors to Vitoria-Gasteiz to complement their visit and experience in a virtual way through participation in a game - `in situ' on the streets of the 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.

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)0.000
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: none
Teacher disagreement score0.907
Threshold uncertainty score0.565

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.025
GPT teacher head0.231
Teacher spread0.205 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations9
Published2012
Admission routes1
Has abstractyes

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