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Record W2020301379 · doi:10.1007/s13361-013-0738-2

Can the Effective Potential of a Linear Quadrupole be Extended to Values of the Mathieu Parameter <b><i>q</i></b> Up to 0.90?

2013· article· en· W2020301379 on OpenAlexafffund
Cong-Zhang Gao, D. J. Douglas

Bibliographic record

VenueJournal of the American Society for Mass Spectrometry · 2013
Typearticle
Languageen
FieldDecision Sciences
TopicScientific Measurement and Uncertainty Evaluation
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsChemistryQuadrupoleMathieu functionComputational chemistryAnalytical Chemistry (journal)Atomic physicsPhysicsQuantum mechanicsChromatography

Abstract

fetched live from OpenAlex

The motion of ions in a linear quadrupole is usually described by solutions to the Mathieu equation. A simplifying approximation to this theory that is widely used for low values of the Mathieu parameters a and q describes ion motion in an effective potential. In this work, we have calculated the effective potential for any q from displacements of calculated ion trajectories caused by a dipole DC electric field. It is assumed that the dipole DC electric field at the center of the displaced trajectory is countered by an "effective" electric field. For all q values, the effective electric field is found to increase linearly with the distance from the center of the quadrupole. The trapping forces probed in this way increase continuously with q up to the first stability region boundary at q=0.908. The well depth (D) at any q can be described by D = q[V(rf)/c], where c=3.955±0.005, very similar to the standard effective potential model with c=4.000.

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)0.001
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.003
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.061
GPT teacher head0.366
Teacher spread0.304 · 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 designSimulation or modeling
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

Citations11
Published2013
Admission routes2
Has abstractyes

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