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Record W4377193375 · doi:10.5539/ies.v16n3p70

Augmented Reality Mobile Application: A New Media of Thai Buddhist Temple History Learning

2023· article· en· W4377193375 on OpenAlexvenueno aff
Somchai Muangmool, Benjamas Phutthima, Narisara Pasitwilaitham, Kanok-on Sirithiti

Bibliographic record

VenueInternational Education Studies · 2023
Typearticle
Languageen
FieldComputer Science
TopicAugmented Reality Applications
Canadian institutionsnot available
FundersNational Research Council of ThailandLampang Rajabhat University
KeywordsAugmented realityBuddhismCultural heritageMultimediaDestinationsTourismMobile deviceComputer scienceVisual artsWorld Wide WebArtHuman–computer interactionHistory

Abstract

fetched live from OpenAlex

Augmented Reality (AR) is defined as an imaginal technology that integrates virtual technology into its surroundings. Today, very few mobile applications provided information either on religious tourism destinations or tourist attractions in Thailand. Hence, it is crucial to develop a digital medium to ease concerns. A collection of different multimedia content forms; images, 3D, text, video, and audio, will display information on an AR application for Pong Sanuk Temple. Five items of content analysis and three items of application performance assessment evaluated by expertise are more than 4.5 scores. Thirty visitors contributed 4.56 ± 0.50 scores of user experience analysis. As the results, the developed AR application for Pong Sanuk Temple is to be used as the medium for cultural heritage preservation as well as to be an innovative idea that provides an immersive experience to visitors, allowing them to learn more about the Thai Buddhist Temple’s culture and history.

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)0.000
metaresearch head score (Gemma)0.001
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.001

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 head0.125
GPT teacher head0.398
Teacher spread0.273 · 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
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

Citations0
Published2023
Admission routes1
Has abstractyes

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