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Record W7133028190

Evaluation of a transparent two-layer display

2004· dissertation· W7133028190 on OpenAlex
Wael Aboelsaadat

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

VenueTSpace · 2004
Typedissertation
Language
FieldEngineering
TopicAdvanced Optical Imaging Technologies
Canadian institutionsOffice of the Privacy Commissioner of CanadaBibliographical Society of Canada
Fundersnot available
KeywordsLayer (electronics)Task (project management)Transparency (behavior)OverlayPixelObject (grammar)Perception
DOInot available

Abstract

fetched live from OpenAlex

Two layer displays are constructed by overlaying one transparent flat panel on another, with a discernable physical separation between layers. This layout could enhance depth perception and increase the available pixels without increasing the width and height of the display. However, it is unclear if the second physical layer provides any advantage over simple alpha-blended transparency on a single layer display. We investigate this issue in two controlled experiments that compare performance between one and two layer displays in focused and divided attention tasks. Results show that for spatially overlapping objects, performance in a focused attention task is similar for both displays, while performance in a divided attention task is degraded on two layer displays. For spatially non-overlapping objects, performance in a focused attention task is degraded on the two layer display if a distractor object is placed on the front layer. Implications for user interface design are also discussed.

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)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.357
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.067
GPT teacher head0.406
Teacher spread0.339 · 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