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Record W1993953409 · doi:10.3357/asem.2174.2007

Color Vision and Fatigue: An Incidental Finding

2007· article· en· W1993953409 on OpenAlexaff
Jeffery K. Hovis, Shankaran Ramaswamy

Bibliographic record

VenueAviation Space and Environmental Medicine · 2007
Typearticle
Languageen
FieldPsychology
TopicSleep and Work-Related Fatigue
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsColor visionLanternColor discriminationAudiologyPsychologyColor perception testComputer visionArtificial intelligenceMedicineComputer science

Abstract

fetched live from OpenAlex

INTRODUCTION: Because there is little information available as to how fatigue and color vision interact, we present some incidental findings on how a protanope and a color-normal perform on several color vision tests after 1 night of sleep deprivation. CASE REPORT: A series of clinically and occupationally based color vision tests were administered to a protanope and a color-normal subject after they had stayed awake all night and after a regular night's sleep. There was essentially no change in their performance on the clinical color vision tests; however, both did worse on the occupationally based color vision tests after 1 night of sleep deprivation. The color-normal made numerous errors in identifying colors displayed on a video display terminal, whereas the protanope had a large increase in errors on the CN Lantern Test. Both subjects passed these respective tests after a regular night's sleep. CONCLUSIONS: The primary reason for color-normal's poor performance on the video display terminal test was probably due to a decrement in his visuo-motor control rather than a loss in color discrimination. In contrast, the protanope's poor performance on the lantern test was probably due to the additive effects of the inherent limitations of his visual system and fatigue.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.207
Threshold uncertainty score0.833

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)0.0010.000

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 head0.021
GPT teacher head0.326
Teacher spread0.305 · 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.

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

Citations2
Published2007
Admission routes1
Has abstractyes

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