MétaCan
Menu
Back to cohort
Record W2006882354 · doi:10.1117/12.567134

Optimizing grating-based devices with the volume current method

2004· article· en· W2006882354 on OpenAlexaff
Robert B. Walker, Stephen J. Mihailov, Ping Lü, Dan Grobnic

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2004
Typearticle
Languageen
FieldMaterials Science
TopicOptical Coatings and Gratings
Canadian institutionsCommunications Research Centre Canada
Fundersnot available
KeywordsGratingComputer scienceBlazed gratingFiber Bragg gratingPolarization (electrochemistry)Electronic engineeringCurrent (fluid)OpticsDiffraction gratingPhysicsEngineeringElectrical engineeringOptical fiberTelecommunications

Abstract

fetched live from OpenAlex

It has been known for sometime that the tap angles associated with slanted, tilted and blazed Bragg grating structures can be affected by the guided mode's state of polarization (SOP). Recently this polarization dependent out-coupling has been employed in order to develop a number of useful devices including in-line polarimeters and PDL equalizers. Although a variety of tools are available to model blazed fibre Bragg grating (FBG) characteristics, a simplified explanation of the fundamental dependencies and potential behaviour has never been fully presented in the literature, making the optimization of these devices difficult and elusive at times. In this submission we present a thorough, intuitive discussion of these trends and possibilities as observed through an extensive theoretical analysis rooted in the Volume Current Method (VCM). In addition to discussing the potential limitations and shortcomings of this formulation, some rough guidelines for the manufacture of various devices are also disclosed.

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)0.000
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.254
Teacher spread0.241 · 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 designSimulation or modeling
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

Citations2
Published2004
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicOptical Coatings and GratingsFrench-language works237,207