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The Accuracy and Precision Performance of Four Videokeratoscopes in Measuring Test Surfaces

2000· article· en· W1531505245 on OpenAlexaff
Wilfred Tang, Michael Collins, Leo G. Carney, Brett A. Davis

Bibliographic record

VenueOptometry and Vision Science · 2000
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topic3D Surveying and Cultural Heritage
Canadian institutionsVictoria Park
Fundersnot available
KeywordsOpticsAccuracy and precisionPolymethyl methacrylateRaster graphicsTest (biology)MathematicsComputer scienceComputer visionPhysicsGeologyStatisticsNuclear magnetic resonance

Abstract

fetched live from OpenAlex

In this study we evaluated the accuracy and precision of three placido-disk videokeratoscopes (the Keratron, Medmont and TMS) and one videokeratoscope that uses the raster-stereogrammetry technique (PAR-CTS) in elevation topography with six test surfaces. The test surfaces were a sphere, an asphere, a multicurve, and three bicurve surfaces. Each instrument performed well on certain test surfaces, but none of the instruments excelled on all of the surfaces. The results showed high accuracy of the Keratron and Medmont instruments in measuring the sphere, asphere, and multicurve surfaces, but not the bicurve surfaces. The precision of the Keratron and Medmont instruments were high. The TMS and PAR-CTS instruments showed poorer accuracy than the Keratron and Medmont instruments for the multicurve test surface but showed better performance for the bicurve surfaces. The PAR-CTS had the poorest performance in precision of the four instruments. The use of the Noryl spherical test surface instead of polymethyl methacrylate (PMMA) resulted in small differences in the accuracy performance of the placido-disk videokeratoscopes only.

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)0.000
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 score0.356
Threshold uncertainty score0.442

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.023
GPT teacher head0.326
Teacher spread0.303 · 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

Citations1
Published2000
Admission routes1
Has abstractyes

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