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Record W3131953539 · doi:10.1177/1071181320641492

A Mirror-in-the-Sky Navigation Aid: Summary of Qualitative Feedback from Soldier and Civilian Users

2020· article· en· W3131953539 on OpenAlexaff
Holland Vasquez, Adam J. Reiner, Greg A. Jamieson, Justin G. Hollands

Bibliographic record

VenueProceedings of the Human Factors and Ergonomics Society Annual Meeting · 2020
Typearticle
Languageen
FieldPsychology
TopicHuman-Automation Interaction and Safety
Canadian institutionsDefence Research and Development CanadaUniversity of Toronto
Fundersnot available
KeywordsFlexibility (engineering)Context (archaeology)Computer scienceHuman–computer interactionTask (project management)CategorizationField (mathematics)Turn-by-turn navigationArtificial intelligenceEngineeringGeographySystems engineering

Abstract

fetched live from OpenAlex

We tested the efficacy of a novel augmented reality navigation aid, called SkyMap, which presents survey navigation information in the sky, above the user. Soldier and civilian users completed a route following task using Mirror-in-the-Sky (a virtual reality prototype of the SkyMap concept), a north-up map, and a track-up map. In this paper, we analyzed and presented a summary of qualitative user feedback and compare feedback from soldier and civilian users. Specifically we used affinity diagrams to categorize verbal qualitative feedback, which led us to identify six themes: 1) an interest in MitS as an innovative navigation display, 2) challenges with identifying turns and orienting, 3) learning MitS over time, 4) the position of MitS in the field of view, 5) customizability and flexibility and 6) ) use in the context of a military operation. We discuss these themes and highlight areas for design improvements.

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)0.001
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.173
Threshold uncertainty score0.484

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.053
GPT teacher head0.335
Teacher spread0.283 · 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 designQualitative
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

Citations4
Published2020
Admission routes1
Has abstractyes

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