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Record W3014744909 · doi:10.1002/col.22499

Color vision defectives' experience: When white is green

2020· article· en· W3014744909 on OpenAlexafffund
Ali Almustanyir, Jeffery K. Hovis

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.212
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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations7
Published2020
Admission routes2
Has abstractyes

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