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Record W4389787581 · doi:10.1051/shsconf/202318003015

X link color blind: A systematic review of congenital color vision deficiency cognitively and neurologically

2023· review· en· W4389787581 on OpenAlexaff
Zeyu Cai

Bibliographic record

VenueSHS Web of Conferences · 2023
Typereview
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsColor Vision DefectsGamutColor visionColor perception testTrichromacyColour VisionLightnessPerceptionCompensation (psychology)AchromatopsiaFeature (linguistics)Artificial intelligenceColor discriminationComputer visionPsychologyAudiologyMedicineComputer scienceOphthalmologyNeuroscienceSocial psychology

Abstract

fetched live from OpenAlex

Color vision deficiency (CVD) can affect people’s perception and limits what job they takes. In order to distinguish colors, different cones differ in spectral sensitivity to capture photons. Several genes (OPN1LW, OPN1MW, ATF6, CNGA3, CNGB3, GNAT2, PDE6H, and PDE6C) are responsible for color vision deficiency. Mutation in these genes can cause deficiency in cones, which will result in reduction in color vision sensitivity. Gene therapy that target these genes showed prominent results in augmenting color vision, yet such methods remain in development and not widely used as treatment. CIE diagram shows the gamut difference in color vision deficiency individuals, and predicts how would the world looks to them. According to reduction theory, the CVD patient would be biased toward the color based on their intact gamut. Compensation glasses showed improved performance in Ishihara’s test, however, other measuring method was not used, furthermore, it is effectiveness on other types of color blind remain unknown. Due to the effective recovery of gene therapy and compensation glasses, further study on such methods is recommended for better recovery in CVD patients.

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 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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.039
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.182
GPT teacher head0.408
Teacher spread0.226 · 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 teacher head, not a consensus.

Study designSystematic review
Domainnot available
GenreReview

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

Citations0
Published2023
Admission routes1
Has abstractyes

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