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Record W2009473211 · doi:10.1002/jms.1190310603

Frequency-sweep fourier tranform ion cyclotron resonance spectroscopy

2007· article· en· W2009473211 on OpenAlexaff
Melvin B. Comisarow, Alan G. Marshall

Bibliographic record

VenueJournal of Mass Spectrometry · 2007
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Resonator Technologies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsFourier transform ion cyclotron resonanceIon cyclotron resonanceChemistryCyclotronFourier transformSelected ion monitoringIonFourier transform spectroscopyCyclotron resonanceNuclear magnetic resonanceAnalytical Chemistry (journal)Mass spectrometryAtomic physicsPhysicsInfrared spectroscopy

Abstract

fetched live from OpenAlex

A single ion cyclotron resonance (ICR) absorption spectrum showing both CH and CH signals has been obtained by exciting both ion cyclotron resonances with a frequency-swept rf irradiation, followed by broad-band detection, digitization of the (timedomain) response, and finally discrete Fourier transformation to produce the (frequency-domain) spectrum. Pulsed-excitution Fourier transform ICR has demonstrated the use of broad-band detection in rapid generation of ICR spectra by Fourier transform methods; this paper demonstrates that frequency-sweep excitation can provide the broad-band irradiation required to excite ion cyclotron resonances throughout any desired mass range. It will thus be possible to obtain an ICR absorption spectrum of given mass range, signal-to-noise ratio, and resolution in an observation period which is two orders of magnitude shorter than that needed to obtain the same spectrum by conventional slow-sweep detection.

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 score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.006
GPT teacher head0.222
Teacher spread0.216 · 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

Citations0
Published2007
Admission routes1
Has abstractyes

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