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A New Measure for Optical Performance

2003· article· en· W7143853121 on OpenAlexaff
Habib Hamam

Bibliographic record

VenueOptometry and Vision Science · 2003
Typearticle
Languageen
FieldEngineering
TopicAdvanced optical system design
Canadian institutionsAssociation for Canadian Studies
Fundersnot available
KeywordsOptical transfer functionWavefrontAdaptive opticsRoot mean squareMeasure (data warehouse)Metric (unit)Transfer functionModulation (music)Sensitivity (control systems)

Abstract

fetched live from OpenAlex

ABSTRACT: Because diffraction and aberration affect the performance of the optical system, a new metric is advanced that emphasizes the link between these three aspects. In general, the modulation transfer function is used as a measure for contrast sensitivity reduction, whereas the root mean square error is often used to quantify the optical quality of the system. However, for a given object, wavefront aberrations can alter the output image very differently even though they have the same root mean square error and modulation transfer function profile. A distinction between coherent and incoherent illumination is made, and a new measure, called optical transfer error, is defined to characterize optical performance complementarily to root mean square and modulation transfer function. The optical transfer error measures the optical excellence in terms of the shape of the wavefront as well as the theoretical performance results. Several illustrations are presented to better discuss optical performance.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.007
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.002
Science and technology studies0.0010.002
Scholarly communication0.0030.006
Open science0.0010.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0070.003

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 head0.016
GPT teacher head0.356
Teacher spread0.340 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreMethods

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
Published2003
Admission routes1
Has abstractyes

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