MétaCan
Menu
Back to cohort

BUDI: Building Urban Designs Interactively A Spatial-Based Visualization and Collaboration Platform for Urban Planning

2021· article· en· W4200534583 on OpenAlexaff
Xi Sun, Matthew Plaudis, Yvonne Coady

Bibliographic record

Venue2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON) · 2021
Typearticle
Languageen
FieldComputer Science
TopicData Visualization and Analytics
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsVisualizationComputer scienceHuman–computer interactionServerData visualizationMultimediaWorld Wide WebArtificial intelligence

Abstract

fetched live from OpenAlex

BUDI (Building Urban Designs Interactively) is an integrated 3D visualization and remote collaboration platform for complex urban design tasks. Users with different backgrounds can remotely engage in the entire design cycle, improving the quality of the end result. In BUDI, the display and interactive environment were designed to seamlessly expand beyond a traditional two-dimensional surface into a fully immersive three-dimensional space. Clients on various devices connect with servers for different functionalities tailored for various user groups. A demonstration with a local urban planning use-case shows the costs and benefits of BUDI as a geographic information visualization and spatial-based collaborative platform. We consider the trade-offs encountered when trying to make the collaboration seamless. Specifically, we detail the multi-dimensional data visualization and interaction the platform provides, and outline how users can interact with and analyze various aspects of urban design.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.024
Threshold uncertainty score0.079

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0240.005

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 head0.021
GPT teacher head0.313
Teacher spread0.292 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations0
Published2021
Admission routes1
Has abstractyes

Explore more

Same venue2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)Same topicData Visualization and AnalyticsFrench-language works237,207