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Record W2103614292 · doi:10.1145/1753326.1753337

Effects of interior bezels of tiled-monitor large displays on visual search, tunnel steering, and target selection

2010· article· en· W2103614292 on OpenAlex
Xiaojun Bi, Seok-Hyung Bae, Ravin Balakrishnan

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
TopicInteractive and Immersive Displays
Canadian institutionsUniversity of Toronto
FundersUniversity of Toronto
KeywordsComputer scienceSelection (genetic algorithm)Affect (linguistics)Visual searchHuman–computer interactionSimulationComputer visionArtificial intelligence

Abstract

fetched live from OpenAlex

Tiled-monitor large displays are widely used in various application domains. However, how their interior bezels affect user performance and behavior has not been fully understood. We conducted three controlled experiments to investigate effects of tiled-monitor interior bezels on visual search, straight-tunnel steering, and target selection tasks. The conclusions of our paper are: 1) interior bezels do not affect visual search time nor error rate; however, splitting objects across bezels is detrimental to search accuracy, 2) interior bezels are detrimental to straight-tunnel steering, but not to target selection. In addition, we discuss how inte-rior bezels affect user behaviors, and suggest guidelines for effectively using tiled-monitor large displays and designing user interfaces suited to them.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.087
Threshold uncertainty score0.432

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 head0.004
GPT teacher head0.257
Teacher spread0.254 · 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

Citations43
Published2010
Admission routes2
Has abstractyes

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