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Record W635276009

Interactive Audio Content: An Approach to Audio Content for a Dynamic Museum Experience through Augmented Audio Reality and Adaptive Information Retrieval

2004· article· en· W635276009 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueSummit (Simon Fraser University) · 2004
Typearticle
Languageen
FieldComputer Science
TopicMusic Technology and Sound Studies
Canadian institutionsSimon Fraser University
FundersCanarie
KeywordsExhibitionAugmented realityComputer scienceMultimediaSoundscapeProcess (computing)Human–computer interactionInterface (matter)World Wide WebSound (geography)ArtVisual arts
DOInot available

Abstract

fetched live from OpenAlex

ec(h)o is an “audio augmented reality interface” utilizing spatialized soundscapes and a semantic web approach to information. The paper discusses our approach to conceptualizing museum content and its creation as audio objects in order to satisfy the requirements of the ec(h)o system. This includes, the conceptualizing of information relevant to an existing exhibition design (an exhibition from the Canadian Museum of Nature in Ottawa). We will discuss the process of acquiring, designing and developing information relevant to the exhibition and its mapping to the requirements of adaptive information retrieval and the interaction model. The development of the audio objects is based on an audio display model that addresses issues of psychoacoustics, composition and cognition. The paper will outline the challenges and identify the limitations of our approach.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.808
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.003
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 head0.045
GPT teacher head0.246
Teacher spread0.202 · 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