MétaCan
Menu
Back to cohort
Record W3012700630 · doi:10.1080/00140139.2020.1737738

A mirror in the sky: assessment of an augmented reality method for depicting navigational information

2020· article· en· W3012700630 on OpenAlexaff
Adam J. Reiner, Justin G. Hollands, Greg A. Jamieson, Sabah Boustila

Bibliographic record

VenueErgonomics · 2020
Typearticle
Languageen
FieldEngineering
TopicSpatial Cognition and Navigation
Canadian institutionsDefence Research and Development CanadaUniversity of Toronto
Fundersnot available
KeywordsAugmented realityVirtual realityComputer scienceWorkloadTask (project management)RecallSpatial contextual awarenessHuman–computer interactionComputer visionArtificial intelligenceEngineeringPsychology

Abstract

fetched live from OpenAlex

We investigated the efficacy of a novel augmented reality (AR) navigation display called Mirror in the Sky (MitS). AR displays can reduce the distance between virtual imagery content and the user’s view of the environment but may have limited benefit for depicting map-based survey information. MitS presents a simulated mirror in the upper visual field, which reflects the topographic layout of the terrain in front of the user. In our experiment, 28 participants used MitS and a track-up Map in virtual reality to perform a route confirmation task, which required participants to decide whether a route could be successfully navigated. A post-trial threat location recall task examined spatial awareness. On that task, accuracy, duration, and subjective workload measures favoured the Map. However, participants with virtual reality experience made more accurate route confirmation decisions with MitS than the Map. Practitioner summary: We compared an augmented reality display called Mirror in the Sky (MitS) to a conventional electronic map for route confirmation and threat location tasks. Although the electronic map showed advantages over MitS on some measures, users with some VR experience performed route confirmation more accurately with MitS than a map.

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 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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.512
Threshold uncertainty score0.190

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 head0.038
GPT teacher head0.335
Teacher spread0.296 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations15
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueErgonomicsSame topicSpatial Cognition and NavigationFrench-language works237,207