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Record W2099213543 · doi:10.1364/ao.49.002686

Spatial dependence of surface error slopes on tolerancing panoramic lenses

2010· article· en· W2099213543 on OpenAlex
Jocelyn Parent, Simon Thibault

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueApplied Optics · 2010
Typearticle
Languageen
FieldEngineering
TopicAdvanced Measurement and Metrology Techniques
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsOpticsDistortion (music)Lens (geology)Surface (topology)Spatial frequencyEntrance pupilMonte Carlo methodFocal lengthGeometrical opticsField of viewComputer scienceMaterials sciencePhysicsPupilGeometryMathematics

Abstract

fetched live from OpenAlex

Surface irregularity errors are conventionally used to specify fabrication accuracy of spherical, aspheric, or plane surfaces. However, in some cases, the amplitude of the irregularities fails to fully describe the surface accuracy requirement when the pupil size is small compared to the surface diameter. In such cases, the irregularity slope will induce distortion. A spatially dependent representation of the irregularity slope is proposed and implemented to specify the surface accuracy. As an optical design example, we study in detail the case of the front surfaces of a fish-eye lens and a panomorph lens. Panoramic lenses are characterized by a small entrance pupil and by important distortion. For both lenses, we found that the novel field-dependent mathematical descriptor provided a nearly perfect agreement with Monte Carlo analyses and can be used to specify the spatially dependent irregularity requirement. The approach is not limited to wide-angle lenses.

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.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.529

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.013
GPT teacher head0.231
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