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Record W2550557946 · doi:10.1088/2040-8978/19/1/013001

Roadmap on structured light

2016· article· en· W2550557946 on OpenAlexafffund
Halina Rubinsztein‐Dunlop, Andrew Forbes, Michael Berry, Mark R. Dennis, Davıd L. Andrews, Masud Mansuripur, Cornelia Denz, Christina Alpmann, Peter Banzer, Thomas Bauer, Ebrahim Karimi, Lorenzo Marrucci, Miles J. Padgett, Monika Ritsch‐Marte, Natalia M. Litchinitser, N. P. Bigelow, Carmelo Rosales‐Guzmán, Antonella Belmonte, Juan P. Torres, Tyler W. Neely, Mark Baker, Reuven Gordon, Alexander B. Stilgoe, Jacquiline Romero, A. G. White, Robert Fickler, Alan E. Willner, Guodong Xie, Benjamin McMorran, Andrew M. Weiner

Bibliographic record

VenueJournal of Optics · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Optical Sensing Technologies
Canadian institutionsUniversity of VictoriaMax Planck - University of Ottawa Centre for Extreme and Quantum Photonics
FundersAir Force Office of Scientific ResearchEngineering and Physical Sciences Research CouncilBasic Energy SciencesNatural Sciences and Engineering Research Council of CanadaNational Science FoundationUniversity of QueenslandDefense Advanced Research Projects AgencyLeverhulme TrustCanada Excellence Research Chairs, Government of CanadaConsejo Nacional de Ciencia y TecnologíaNational Aeronautics and Space AdministrationQuantICOffice of ScienceCanada Research ChairsU.S. Department of Energy
KeywordsStructured lightComputer scienceLight fieldKey (lock)Perspective (graphical)Face (sociological concept)Data scienceSociologyArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract Structured light refers to the generation and application of custom light fields. As the tools and technology to create and detect structured light have evolved, steadily the applications have begun to emerge. This roadmap touches on the key fields within structured light from the perspective of experts in those areas, providing insight into the current state and the challenges their respective fields face. Collectively the roadmap outlines the venerable nature of structured light research and the exciting prospects for the future that are yet to be realized.

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)0.006
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.033
Threshold uncertainty score0.109

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.002
Scholarly communication0.0050.008
Open science0.0020.005
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0330.012

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 head0.009
GPT teacher head0.248
Teacher spread0.239 · 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 designTheoretical or conceptual
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

Citations1,327
Published2016
Admission routes2
Has abstractyes

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