MétaCan
Menu
Back to cohort
Record W4293793413 · doi:10.24908/cppapc.v2022i1.15440

Virtual Density

2022· article· en· W4293793413 on OpenAlex
Francisco Alaniz Uribe, Bram Van der Heijden

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.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueCanadian Planning and Policy / Aménagement et politique au Canada · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsHeadsetVisualizationHuman–computer interactionVirtual realityComputer scienceProcess (computing)Component (thermodynamics)PerceptionGraphicsComputer graphicsMultimediaInterface (matter)Computer graphics (images)Artificial intelligencePsychologyTelecommunications

Abstract

fetched live from OpenAlex

The process of densification in existing communities is complex and often encounters resistance. Public engagement is a crucial component to this process and requires appropriate visual and spatial communication tools. This pilot project explored the use of Virtual Reality (VR) as a spatial communication tool that uses CAD graphics with new visualization technology to provide the public with an immersive experience in a virtual environment. Using a pair of VR goggles and a digital 3D model, different scenarios were presented to members of the public to test their perception of various density models. Three density scenarios were presented to the public, both in the form of traditional posters and using a headset and VR computer model. We found that the public were able to better understand the scenarios and were more accepting of densification when while visualizing the proposed density scenarios via the VR interface.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.244
Threshold uncertainty score0.996

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)0.0120.000

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 head0.016
GPT teacher head0.246
Teacher spread0.230 · 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