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Record W2053479348 · doi:10.1145/2669485.2669555

Bancada

2014· article· en· W2053479348 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicData Visualization and Analytics
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsZoomGeospatial analysisComputer graphics (images)Human–computer interactionComputer scienceAffordancePhysicsLens (geology)CartographyOptics

Abstract

fetched live from OpenAlex

Nowadays, looking at the path between two points on a city map has become a simple task using any modern tablet, smartphone or laptop. However, when exploring maps with different information across multiple layers and scales, users experience information discontinuity. Bancada is a multi-display system developed to investigate the exploration of geospatial information using multiple mobile devices in a multi-display environment. In Bancada, tablets are Zoomable Magic Lenses that augment, through specific geospatial layers, an overview map displayed on a tabletop or on a wall display. Users interact with lenses using touch gestures to pan and zoom; and multi-layer maps can be built by overlapping different lenses. Currently, Bancada is being used to research user interfaces separated across multiple devices and interactions with high-resolution mobile devices. Future work with Bancada includes (i) evaluating the user performance when using one tablet or multiple tablets to control all lenses; (ii) exploring what and how interactions can be performed on an overview map; and (iii) exploring how lenses can be changed.

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 score0.965
Threshold uncertainty score0.411

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 science0.0000.000
Research integrity0.0000.000
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 head0.015
GPT teacher head0.274
Teacher spread0.259 · 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

Citations10
Published2014
Admission routes2
Has abstractyes

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