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Record W4407186083 · doi:10.1364/josaa.538775

Resolution metric for imaging systems

2025· article· en· W4407186083 on OpenAlexaff
Brad C. Smith, Phil Gatt

Bibliographic record

VenueJournal of the Optical Society of America A · 2025
Typearticle
Languageen
FieldEngineering
TopicOptical Systems and Laser Technology
Canadian institutionsLockheed Martin (Canada)
Fundersnot available
KeywordsMetric (unit)Resolution (logic)MathematicsComputer scienceArtificial intelligenceComputer visionEngineering

Abstract

fetched live from OpenAlex

The resolution of an imaging system is an important metric for quantifying its optical performance. Commonly used metrics like the instantaneous field of view and the Rayleigh criterion have multiple drawbacks, e.g., they ignore aberrations and obscurations, assume spatially uniform detector response, and treat detector and optical resolutions separately. Here, we present a new metric, Ω res , based on the point response function of an imaging system and demonstrate its advantages. We also relate this new metric to the matched filter signal-to-noise ratio and define a generalized two-dimensional sampling Q number.

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)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.011
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.002
Science and technology studies0.0010.002
Scholarly communication0.0030.006
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.002

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 head0.005
GPT teacher head0.223
Teacher spread0.218 · 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 designNot applicable
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
Published2025
Admission routes1
Has abstractyes

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Same venueJournal of the Optical Society of America ASame topicOptical Systems and Laser TechnologyFrench-language works237,207