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Color Vision and the Railways

2014· article· en· W627566237 on OpenAlexfundaboutno aff
Stephen J. Dain, Armand Casolin, Jennifer Long

Bibliographic record

VenueOptometry and Vision Science · 2014
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsnot available
FundersUniversity of Waterloo
KeywordsLanternTrichromacyColor visionColor discriminationColor perception testColor Vision DefectsArtificial intelligenceComputer visionCategorizationOptometryColour VisionComputer scienceMedicine

Abstract

fetched live from OpenAlex

PURPOSE: There are two currently available lantern tests that have their design based solely on the practices of the rail industry. These are the CN Lantern (CNLAN) used on the Canadian Railways and the Railway LED Lantern Tests (RLLT). In the same way that the signaling practices differ in the two jurisdictions, the design of the lanterns also differ. The CIE (Commission Internationale de l'Éclairage)-recommended color vision standards for transport predate both lanterns. The study was undertaken to assess the appropriateness of these lanterns in CIE Color Vision Standard 1. CIE Standard 1 is called "Normal color vision" but some very mild anomalous trichromats may pass the specified "lantern test that presents a high level of difficulty." METHODS: The lantern tests were undertaken by 46 color vision-normal and 37 color vision-deficient subjects. RESULTS: Subjects made more errors on the RLLT, and the pattern of errors is different, partly because there are blank presentations in the RLLT and "no light" is an acceptable response. The two lanterns showed agreement on the pass/fail categorization of 73 of the 83 subjects. The RLLT fails more color vision-normal subjects. CONCLUSIONS: Despite the different construction principles, the RLLT and CNLAN have pass/fail levels that are comparable with the Holmes-Wright Type B lantern, which is nominated in CIE Color Vision Standard 1 but is no longer commercially available. The higher failure rate of color vision-normal subjects on the RLLT has been addressed by changing the intensities of the two darkest red lights.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.345
Threshold uncertainty score0.919

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0000.000
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 head0.031
GPT teacher head0.437
Teacher spread0.407 · 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 designBench or experimental
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

Citations9
Published2014
Admission routes2
Has abstractyes

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