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Record W4360993817 · doi:10.1109/tim.2023.3261906

Rapid Electromagnetic Induction Imaging With an Optically Raster-Scanned Atomic Magnetometer

2023· article· en· W4360993817 on OpenAlexfundno aff
Benjamin Maddox, Cameron Deans, Han Yao, Y. Cohen, Ferruccio Renzoni

Bibliographic record

VenueIEEE Transactions on Instrumentation and Measurement · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAtomic and Subatomic Physics Research
Canadian institutionsnot available
FundersEngineering and Physical Sciences Research CouncilFaculty of Arts and Social Sciences, Carleton UniversityDefence Science and Technology Laboratory
KeywordsRaster scanRaster graphicsEMIElectromagnetic interferenceMagnetometerRadio frequencyElectromagnetic inductionElectrical conductorMaterials scienceOpticsBiomagnetismPhysicsMagnetic fieldComputer scienceElectromagnetic coilTelecommunicationsArtificial intelligence

Abstract

fetched live from OpenAlex

We present an apparatus to overcome the limitations of mechanical raster-scanning in electromagnetic induction imaging (EMI) techniques by instead performing a 2D optical raster-scan within the vapour cell of a radio-frequency atomic magnetometer (RF-AM). A large cuboidal <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">87</sup> Rb vapour cell is employed to act as the medium of an RF-AM with the pump and probe beams translated in the cell via acousto-optics. The technique is shown to give robust and repeatable magnetic measurements over the cell volume and successfully resolves conductive targets with EMI. Optical raster-scanning removes the limitation of slow mechanical actuation and a fast imaging procedure is enacted resolving conductive targets at a rate of 40 ms/pixel.

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 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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.939
Threshold uncertainty score0.679

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 head0.033
GPT teacher head0.271
Teacher spread0.238 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations10
Published2023
Admission routes1
Has abstractyes

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