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Record W3014744909 · doi:10.1002/col.22499

Color vision defectives' experience: When white is green

2020· article· en· W3014744909 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

VenueColor Research & Application · 2020
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsUniversity of Waterloo
FundersDefence Research and Development CanadaKing Saud University
KeywordsColor visionContext (archaeology)Artificial intelligenceWhite (mutation)IconComputer visionComputer sciencePsychologyGeographyBiology

Abstract

fetched live from OpenAlex

Abstract Despite having difficulties in discriminating colors, individuals with a congenital color vision defect (CVD) sometimes identify colors correctly. One explanation for their correct use of the color name could be the context in which the color is used. In North America, how context influences the use of color names can be illustrated using the pedestrian signal light, where the color of the light indicating that it is safe to cross is white. Sixty color vision normals (CVNs) and 68 CVD subjects were asked to identify the color of the man figure icon from memory. All of the CVNs identified the figure correctly as white, whereas 56% of the CVDs identified the color of the man‐figure incorrectly, with 92% of the errors identifying the color as green and the remaining 8% as yellow. No one identified the figure as red. The data show one example of how context plays a role in how CVDs identify colors.

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 categoriesInsufficient payload (model declined to judge)
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 score0.247
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.003

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 head0.212
GPT teacher head0.466
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