MétaCan
Menu
Back to cohort
Record W4390082878 · doi:10.1364/optcon.509943

Enhanced sensitivity distributed sensing of magnetic fields in optical fiber using random Bragg grating

2023· article· en· W4390082878 on OpenAlexafffund
Antoine Leymonerie, Jean-Sébastien Boisvert, Léonie Juszczak, Sébastien Loranger

Bibliographic record

VenueOptics Continuum · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsPolytechnique Montréal
FundersPolytechnique Montréal
KeywordsReflectometryOpticsFaraday effectImage resolutionFiber Bragg gratingSensitivity (control systems)Optical fiberMaterials sciencePhysicsGratingNoise (video)Fiber optic sensorPolarization (electrochemistry)Magnetic fieldTime domainElectronic engineering

Abstract

fetched live from OpenAlex

We show that the use of random optical grating using UV exposure (ROGUE) can significantly reduce the noise floor of an optical frequency domain reflectometry (OFDR) measurement of Faraday rotation in the polarization. We compare it with unexposed spun fiber, which shows a S/P minimum ratio (signal noise floor) 20 dB higher than when using our ROGUE. High sensitivity magnetic field measurements are achieved by spatially filtering (setting a gage length) the derivative of the S/P ratio’s evolution. An example of a calibrated electromagnet spatially resolved B-field measurement is demonstrated, which can measure fields down to 10 mT with 10 cm spatial resolution. The potential for current sensing using the ROGUE apparatus is discussed and simulation shows a noise floor of ∼1 A with 40 probing loops spatial resolution.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.000
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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 head0.011
GPT teacher head0.234
Teacher spread0.222 · 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 designBench or experimental
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

Citations4
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueOptics ContinuumSame topicAdvanced Fiber Optic SensorsFrench-language works237,207