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IMPACT OF VIRTUAL REALITY EXPERIENCE ON ACCESSIBILITY OF CULTURAL HERITAGE

2019· article· en· W2945514237 on OpenAlexafffund
A. Paladini, A. Dhanda, M. Reina Ortiz, A. Weigert, Eslam Nofal, A. Min, M. Gyi, Simon Su, Koenraad Van Balen, Mario Santana Quintero

Bibliographic record

Venue˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topic3D Surveying and Cultural Heritage
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCultural heritageVirtual realityAugmented realityValue (mathematics)Natural (archaeology)Environmental resource managementComputer scienceGeographyHuman–computer interactionArchaeology

Abstract

fetched live from OpenAlex

Abstract. Accessibility plays a main role among the aspects that contribute to the conservation of Cultural Heritage sites. Seismic stability, fragility of the artefacts, conflicts, deterioration, natural disasters, climate change and visitors’ impact are only some of the possible causes that might lead to the inaccessibility of a heritage site for both researchers and visitors.The increasing potential of Information and Communication Technologies (ICT) in the conservation field has resulted in the development of Augmented and Virtual reality (AR and VR) experiences. These ones can be very effective for what concerns the description of the visual experience, but also improve the understanding of a site and even became analytic research tools.This paper presents an inaccessible Buddhist temple in the Myanmar city of Bagan as a case study for the realization of a VR experience that aims at providing accessibility to knowledge and therefore a better understanding of the cultural value. In order to evaluate the effectiveness of the VR for this purpose, a user study has been conducted and its results are reported.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.969
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.003
Scholarly communication0.0000.001
Open science0.0020.000
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 head0.026
GPT teacher head0.285
Teacher spread0.259 · 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.

Study designOther design
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

Citations49
Published2019
Admission routes2
Has abstractyes

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