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Record W4366985278 · doi:10.26434/chemrxiv-2023-6g77r

How Effective Are Indicators for Individuals with Colour Vision Deficiency?

2023· preprint· en· W4366985278 on OpenAlexafffund
Nicholas J. Roberts, Toren Hynes, Devon Stacey, Jennifer L. MacDonald

Bibliographic record

VenueChemRxiv · 2023
Typepreprint
Languageen
FieldPsychology
TopicColor perception and design
Canadian institutionsDalhousie University
FundersDalhousie University
KeywordsPerspective (graphical)TrichromacyPsychologyTest (biology)Computer scienceOptometryArtificial intelligenceColor visionMedicineBiology

Abstract

fetched live from OpenAlex

Coloured indicators, whether solution or paper based, are often used in laboratory courses and academic/industrial research as a qualitative method to test important experimental markers. While useful, these tools present challenges to those with colour vision deficiency (CVD), who are unable to interpret the same results as their peers. What’s more, some of these tools aren’t as useful in determining important reaction specifics. This commentary presents the perspective of four individuals, three with CVD and one with trichromatic (normal) vision, on how easily coloured indicators are interpreted and how we can address any difficulties in a laboratory setting.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.655
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.043
GPT teacher head0.343
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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

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

Citations0
Published2023
Admission routes2
Has abstractyes

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