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Record W611415660

International optical design conference 2006 : 4-8 June 2006, Vancouver, British Columbia, Canada

2006· book· en· W611415660 on OpenAlexaboutno aff
Modelling, G. Groot Gregory, Joseph M. Howard, R. John Koshel

Bibliographic record

VenueSPIE eBooks · 2006
Typebook
Languageen
FieldPhysics and Astronomy
TopicAdaptive optics and wavefront sensing
Canadian institutionsnot available
Fundersnot available
KeywordsTrilogyComputer scienceOpticsArtificial intelligenceArt historyHistoryPhysics
DOInot available

Abstract

fetched live from OpenAlex

[Show Abstracts] To view an abstract for an individual paper, or place a paper order, click the paper title. 634201 The first optical convention (in English): the 1905 Optical Convention in London, England Kevin P. Thompson 634202 Double Gauss lens design: a review of some classics Reginald P. Jonas, Michael D. Thorpe 634203 Twenty-first century optical tolerancing: a look at the past and improvements for the future Richard N. Youngworth 634204 Alignment of optical systems Robert E. Parks 634205 Use of an application programming interface (API) to allow non-optical designers to perform specific optical evaluations Mark C. Sanson 634206 The current state of the international standard for exchange of optical data in electronic form Prudence M. J. H. Wormell 634207 Wavefront correction using micromirror arrays: comparing the efficacy of tip-tilt-piston and piston-only micromirror arrays William C. Sweatt, Olga B. Spahn, William D. Cowan, David V. Wick 634208 Interpretation of pupil aberrations in imaging systems Jose Sasian 634209 General sine condition for plane-symmetric imaging systems and some example aplanatic designs Chunyu Zhao 63420A Third-order aberrations of an AGRIN thin lens as a function of the shape and conjugate variables Jose A. Diaz, Carles Pizarro, Josep Arasa.

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)0.001
metaresearch head score (Gemma)0.001
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: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.876
Threshold uncertainty score0.901

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0040.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.2690.129

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 head0.012
GPT teacher head0.199
Teacher spread0.187 · 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
GenreOther

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
Published2006
Admission routes1
Has abstractyes

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