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Record W4282961824 · doi:10.21606/drs.2022.699

Designing a tangible augmented reality experience for cultural heritage research

2022· article· en· W4282961824 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueProceedings of DRS · 2022
Typearticle
Languageen
FieldComputer Science
TopicAugmented Reality Applications
Canadian institutionsUniversity of TorontoToronto Metropolitan University
FundersConnaught FundOntario Ministry of Research and InnovationSocial Sciences and Humanities Research Council of CanadaCanada Research Chairs
KeywordsAugmented realityCultural heritageComputer scienceProcess (computing)Mixed realityObject (grammar)Human–computer interactionHistoryArchaeologyArtificial intelligence

Abstract

fetched live from OpenAlex

The Tangible Augmented Reality Archives (TARA) is an augmented reality system developed to assist cultural heritage researchers in remotely collecting and assessing information on rare artifacts. Building on prior research, we designed TARA to address challenges faced by cultural heritage researchers, including limited access to collections, as well as the time and budget constraints associated with archival visits. In this paper, we examine the use of augmented reality to advance cultural heritage research, and describe a series of design explorations that explore tangible interactions with remote cultural heritage artifacts. These include a three-dimensional cube design, a two-dimensional prop design and an object-based design. We conclude with a discussion of lessons learned from our design process and how this will impact future designs.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.427
Threshold uncertainty score0.616

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.146
GPT teacher head0.398
Teacher spread0.252 · 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