MétaCan
Menu
Back to cohort
Record W3207920315 · doi:10.3390/mti5100064

An Overview of Olfactory Displays in Education and Training

2021· article· en· W3207920315 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

VenueMultimodal Technologies and Interaction · 2021
Typearticle
Languageen
FieldNeuroscience
TopicOlfactory and Sensory Function Studies
Canadian institutionsOntario Tech UniversityAlgoma University
FundersNatural Sciences and Engineering Research Council of CanadaAlgoma University
KeywordsMemorizationOlfactionComputer scienceHuman–computer interactionInterface (matter)Sensory systemRecallTraining (meteorology)PsychologyMultimediaCognitive psychologyNeuroscience

Abstract

fetched live from OpenAlex

This paper describes an overview of olfactory displays (human–computer interfaces that generate and diffuse an odor to a user to stimulate their sense of smell) that have been proposed and researched for supporting education and training. Past research has shown that olfaction (the sense of smell) can support memorization of information, stimulate information recall, and help immerse learners and trainees into educational virtual environments, as well as complement and/or supplement other human sensory channels for learning. This paper begins with an introduction to olfaction and olfactory displays, and a review of techniques for storing, generating and diffusing odors at the computer interface. The paper proceeds with a discussion on educational theories that support olfactory displays for education and training, and a literature review on olfactory displays that support learning and training. Finally, the paper summarizes the advantages and challenges regarding the development and application of olfactory displays for education and training.

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 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 score0.453
Threshold uncertainty score0.278

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 studies0.0000.000
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 head0.270
GPT teacher head0.363
Teacher spread0.093 · 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