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Record W6922199568 · doi:10.11575/prism/36394

Simultaneous Worlds: Using Physical Models to Contextualize and Compose Visualizations

2018· other· en· W6922199568 on OpenAlexfundno aff

Bibliographic record

VenueOpen MIND · 2018
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaAlberta InnovatesAlberta Innovates - Technology Futures
KeywordsEmbodied cognitionMetaphorScale (ratio)VisualizationData visualizationEnergy (signal processing)ArchitectureAugmented reality

Abstract

fetched live from OpenAlex

In this poster, we introduce an interactive prototype that integrates site-specific architectural models and tangible displays to compose multiple data representations in the same view. This vision of simultaneous worlds uses physical models as a substrate upon which visualizations of multiple data streams can be dynamically integrated. To explore the potential of this concept, we built a tangible tabletop system that uses scale models of a campus to visualize energy use and climate data. We believe that the metaphor of simultaneous worlds has the ability to unpack novel connections between datasets, supporting embodied exploration, critical thinking, and collaboration.

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.018
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.002
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0070.008
Open science0.0020.010
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0180.003

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 head0.098
GPT teacher head0.399
Teacher spread0.301 · 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 designTheoretical or conceptual
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
Published2018
Admission routes1
Has abstractyes

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