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Record W2566723838 · doi:10.1103/physreva.92.052504

Frequency-offset separated oscillatory fields

2015· article· en· W2566723838 on OpenAlexafffund
Amar C. Vutha, E. A. Hessels

Bibliographic record

VenuePhysical Review A · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicQuantum optics and atomic interactions
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsPhysicsOffset (computer science)Frequency offsetRelative phaseSIGNAL (programming language)Computational physicsAcousticsPhase (matter)Nuclear magnetic resonanceQuantum mechanicsTelecommunications

Abstract

fetched live from OpenAlex

A frequency-offset separated-oscillatory-field technique is presented. The technique is a modification of the Ramsey method of separated oscillatory fields [Phys. Rev. 76, 996 (1949)], in which the frequencies of the two separated oscillatory fields are slightly offset from each other, so that the relative phase of the two fields varies continuously with time. With this technique, the detection signal oscillates in time at the offset frequency, and the resonance frequency is obtained by using a simple straight-line fit of the phase of this signal. The technique has the advantages of being insensitive to the frequency response of the experimental system, of being sensitive only to noise at the offset frequency, and of allowing systematic effects to be more cleanly resolved due to the simple lineshape.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.032
GPT teacher head0.349
Teacher spread0.317 · 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

Citations22
Published2015
Admission routes2
Has abstractyes

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