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Record W3129533965 · doi:10.1177/1071181320641281

Augmented Reality Procedure Assistance System for Operator Training and Simulation

2020· article· en· W3129533965 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.

Bibliographic record

VenueProceedings of the Human Factors and Ergonomics Society Annual Meeting · 2020
Typearticle
Languageen
FieldComputer Science
TopicAugmented Reality Applications
Canadian institutionsUniversity of WaterlooUniversity of Toronto
Fundersnot available
KeywordsAugmented realityWorkloadComputer scienceOperator (biology)Human–computer interactionTask (project management)Optical head-mounted displayField (mathematics)Test (biology)SimulationTraining (meteorology)Artificial intelligenceEngineering

Abstract

fetched live from OpenAlex

This study explores the design, implementation, and evaluation of an Augmented Reality (AR) prototype that assists novice operators in performing procedural tasks in simulator environments. The prototype uses an optical see-through head-mounted display (OST HMD) in conjunction with a simulator display to supplement sequences of interactive visual and attention-guiding cues to the operator’s field of view. We used a 2x2 within-subject design to test two conditions: with/without AR-cues, each condition had a voice assistant and two procedural tasks (preflight and landing). An experiment examined twenty-six novice operators. The results demonstrated that augmented reality had benefits in terms of improved situation awareness and accuracy, however, it yielded longer task completion time by creating a speed-accuracy trade-off effect in favour of accuracy. No significant effect on mental workload is found. The results suggest that augmented reality systems have the potential to be used by a wider audience of operators.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.858
Threshold uncertainty score0.518

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 studies0.0010.000
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 head0.042
GPT teacher head0.268
Teacher spread0.226 · 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