MétaCan
Menu
Back to cohort
Record W4225102759 · doi:10.1145/3491101.3519692

MultiFingerBubble: A 3D Bubble Cursor Variation for Dense Environments

2022· article· en· W4225102759 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

VenueCHI Conference on Human Factors in Computing Systems Extended Abstracts · 2022
Typearticle
Languageen
FieldComputer Science
TopicInteractive and Immersive Displays
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersInterregEuropean Regional Development FundEuropean Commission
KeywordsCursor (databases)Computer scienceBubbleVisualizationData visualizationArtificial intelligenceVolume (thermodynamics)3D interactionComputer visionComputer graphics (images)Virtual reality

Abstract

fetched live from OpenAlex

In this work, we propose MultiFingerBubble, a new variation of the 3D Bubble Cursor. The 3D Bubble Cursor is sensitive to distractors in dense environments: the volume selection resizes to snap-to nearby targets. To prevent the cursor to constantly re-snap to neighboring targets, MultiFingerBubble includes multiple targets in the volume selection, and hence increases the targets effective width. Each target in the volume selection is associated with a specific finger. Users can then select a target by flexing its corresponding finger. We report on a controlled in-lab experiment to explore various design options regarding the number of fingers to use, and the target-to-finger mapping and its visualization. Our study results suggest that MultiFingerBubble is best used with three fingers and colored lines to reveal the mapping between targets and fingers.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.405
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.064
GPT teacher head0.310
Teacher spread0.246 · 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