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Record W2323717770 · doi:10.1097/opx.0000000000000519

From Railways to “Soup to Nuts”

2015· editorial· en· W2323717770 on OpenAlexaboutno aff
Tony Adams

Bibliographic record

VenueOptometry and Vision Science · 2015
Typeeditorial
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsnot available
Fundersnot available
KeywordsTheme (computing)Feature (linguistics)Color visionCrashBlindnessOptometryPsychologyComputer scienceArtificial intelligenceMedicine

Abstract

fetched live from OpenAlex

After three Feature single theme issues in 5 months (August, October, and December 2014), readers are likely wondering if that is the new focus of Optometry and Vision Science (OVS). Although we know readers and authors appreciate Feature theme issues, we also know that many general readers like to have a range of interesting topics to update them. That range of topics can be found in this February OVS issue. Hence, from “soup to nuts.” We open with a trip down memory lane for those of you who have a love of railway travel and its accompanying vision implications and standards. Some 140 years ago, there was a now-famous railway crash that took nine lives in Sweden (near Lagerlunda, Sweden, in 1875) and was for most of the intervening 140 years since then attributed to color-defective vision or “color blindness” of the engine driver. Color vision standards for railway locomotive drivers were almost universal within a few years of Holmgren (of the Holmgren wool color vision test) writing that he “supposed that color-blindness was one of the principal causes of the accident.”1 Indeed, so influential was the assertion that color blindness led to the train crash that new attention and color vision standards were introduced and in many ways heralded the beginning of occupational vision standards that became implemented in many countries around the world. One can certainly view the incident as stirring appropriate interest at least in color vision standards for the railways. A series of “lantern” tests of color vision have emerged ever since, some adopted by international standards organizations. That the incident in Sweden was likely not simply caused by defective color vision was only recently elucidated by a fine post hoc evaluation of the details of the actual event by Mollon and Cavonius2 just 3 years ago. They concluded that although the color-defective vision hypothesis was plausible, “there is no firm evidence that color deficiency did cause the collision.” They identified a number of other factors that undoubtedly contributed to the accident. In this February issue of OVS, we lead with three articles that deal with color vision testing in the railways. Our authors introduce a new Railway LED Lantern Test already adopted in one state in Australia. Our authors propose this new lantern test of color vision for railways personnel and compare it favorably with those recommendations made by the international standards community (Commission Internationale de l’Éclairage).3 The only other railways lantern test currently available and in use is the CN Lantern used on the Canadian Railways test. I encourage readers to read these articles; I believe that these papers are very likely the forerunners of the adoption of a very practical modern test for many railroads around the world. We move from color vision tests to six papers on cornea and contact lenses (CLs) that cover CL care and adverse events, simultaneous focus from CL, corneal sensitivity, corneal refractive surgery, a perspective on inflammation and keratoconus, and stereo changes after LASIK (laser-assisted in situ keratomileusis). We follow this with two papers on binocular vision problems, reading and crowding issues, and academic performance and refractive error. We then return to our Clinical Pearls and four papers on clinical cases in the online-only section: they are rich in color images. Tony Adams, OD, PhD, FAAO Editor in Chief Optometry and Vision Science

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)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.296
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.002

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 head0.048
GPT teacher head0.481
Teacher spread0.434 · 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 designNot applicable
Domainnot available
GenreEditorial

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

Citations1
Published2015
Admission routes1
Has abstractyes

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