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Record W3189151684 · doi:10.18260/1-2--38037

Web-based Game vs. Virtual Reality Field Surveying Labs Towards Enhancing Experiential Education

2024· article· en· W3189151684 on OpenAlex
Dimitrios Bolkas, Mojgan Jadidi, Jeffrey Chiampi, Muhammad Usman

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

Venue2021 ASEE Virtual Annual Conference Content Access Proceedings · 2024
Typearticle
Languageen
FieldComputer Science
TopicAugmented Reality Applications
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of CanadaMitacsYork University
KeywordsGeospatial analysisVirtual realityGNSS applicationsComputer scienceField (mathematics)Digital elevation modelGeodetic datumMultimediaHuman–computer interactionRemote sensingGlobal Positioning SystemGeographyCartography

Abstract

fetched live from OpenAlex

He has a diverse geodetic and geoscientific experience that includes terrestrial, mobile, and airborne laser scanning, digital elevation models, unmanned aerial systems, GNSS networks, geoid and gravity-field modeling.His main research interest is on building methods to increase, understand, and assess the quality/uncertainty in 3D geospatial datasets.His research develops new methods and techniques to enhance functionality of 3D geospatial data and models.In addition, recent research interests include utilizing 3D data for creating realistic environments in immersive virtual reality, as well as the application of virtual reality in engineering education.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.740
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0030.004
Open science0.0020.001
Research integrity0.0000.001
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 head0.057
GPT teacher head0.327
Teacher spread0.270 · 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