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Record W2209099854 · doi:10.1126/science.aad2449

Torque-mixing magnetic resonance spectroscopy

2015· article· en· W2209099854 on OpenAlexafffund
Joseph E. Losby, Fatemeh Fani Sani, D. T. Grandmont, M. Belov, Jacob A. J. Burgess, Shawn Compton, Wayne K. Hiebert, D. Vick, Kaveh Mohammad, Elham Salimi, Greg E. Bridges, D. J. Thomson, M. R. Freeman

Bibliographic record

VenueScience · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMechanical and Optical Resonators
Canadian institutionsUniversity of ManitobaNational Institute for NanotechnologyUniversity of Alberta
FundersCanada Research ChairsCanada Foundation for InnovationNational Institute for NanotechnologyAlberta Innovates - Technology FuturesNatural Sciences and Engineering Research Council of CanadaCMC Microsystems
KeywordsFerromagnetic resonanceMagnetic resonance force microscopyCondensed matter physicsResonance (particle physics)Magnetic momentSpectroscopyMagnetizationMesoscopic physicsNuclear magnetic resonanceMaterials scienceTorquePhysicsMagnetic fieldAtomic physics

Abstract

fetched live from OpenAlex

A universal, torque-mixing method for magnetic resonance spectroscopy is presented. In analogy to resonance detection by magnetic induction, the transverse component of a precessing dipole moment can be measured in sensitive broadband spectroscopy, here using a resonant mechanical torque sensor. Unlike induction, the torque amplitude allows equilibrium magnetic properties to be monitored simultaneously with the spin dynamics. Comprehensive electron spin resonance spectra of a single-crystal, mesoscopic yttrium iron garnet disk at room temperature reveal assisted switching between magnetization states and mode-dependent spin resonance interactions with nanoscale surface imperfections. The rich detail allows analysis of even complex three-dimensional spin textures. The flexibility of microelectromechanical and optomechanical devices combined with broad generality and capabilities of torque-mixing magnetic resonance spectroscopy offers great opportunities for development of integrated devices.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.387
Threshold uncertainty score0.412

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 head0.018
GPT teacher head0.275
Teacher spread0.257 · 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 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

Citations44
Published2015
Admission routes2
Has abstractyes

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