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Record W2087357160 · doi:10.1145/2077451.2077484

A high bit depth digital imaging pipeline for vision research

2011· article· en· W2087357160 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
Typearticle
Languageen
FieldPhysics and Astronomy
TopicColor Science and Applications
Canadian institutionsDolby (Canada)
Fundersnot available
KeywordsGamutComputer scienceHigh dynamic rangePipeline (software)Computer visionArtificial intelligencePerceptionGraphics pipelineComputer graphics (images)Pipeline transportPsychophysicsGraphicsHuman visual system modelMaxima and minimaHigh fidelityComputer graphicsDynamic rangeEngineering3D computer graphicsMathematicsPsychologyImage (mathematics)

Abstract

fetched live from OpenAlex

In order to achieve accurate results in user studies in the fields of Psychophysics, Experimental Psychology, Ophthalmology and clinical studies there are high demands towards an imaging pipeline presenting these stimuli in an experiment (as illustrated in Figure 1). For example, display stability and repeatability, both short term and long term are crucial when conducting research leading to robust results. Further important factors are the perceptual limits of a graphics pipeline. Here, two important elements are the achievable dynamic range and the color gamut, which would ideally approximate or exceed the capabilities of the human visual system (HVS). In an optimal solution, those stimulus dimensions would be displayed with continuous intensity levels between their respective extrema (e.g. from dark to light) when presenting them to participants.

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: Empirical · Consensus signal: none
Teacher disagreement score0.792
Threshold uncertainty score0.395

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 head0.069
GPT teacher head0.370
Teacher spread0.300 · 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

Citations1
Published2011
Admission routes1
Has abstractyes

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