MétaCan
Menu
Back to cohort
Record W3048801906 · doi:10.1145/3399715.3399743

Corsican Twin

2020· preprint· en· W3048801906 on OpenAlex

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.

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldComputer Science
TopicAugmented Reality Applications
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsDebuggingSituatedCorsicanComputer scienceVisualizationFormative assessmentHuman–computer interactionContext (archaeology)Augmented realityDomain (mathematical analysis)AffordanceProcess (computing)Virtual realitySoftware engineeringMultimediaProgramming languageArtificial intelligence

Abstract

fetched live from OpenAlex

We introduce Corsican Twin, a tool for authoring augmented reality data visualisations in virtual reality using digital twins. The system provides users with the contextual information necessary to design embedded and situated data visualisations in a safe and convenient remote setting. We created system via a co-design process which involved people with little or no programming experience. Using the system, we illustrate three potential use cases for situated visualizations in the context of building maintenance, including: (1) on-site equipment debugging and diagnosis; (2) remote incident playback; and (3) operations simulations for future buildings. From feedback gathered during formative evaluations of our prototype tool with domain experts, we discuss implications, opportunities, and challenges for future in situ visualisation design tools.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.633
Threshold uncertainty score0.676

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.003
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.049
GPT teacher head0.292
Teacher spread0.243 · 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

Quick stats

Citations64
Published2020
Admission routes1
Has abstractyes

Explore more

Same topicAugmented Reality ApplicationsFrench-language works237,207