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Record W2237155666 · doi:10.1134/s1061934815130067

Simulation of the stationary distributions of ions in radiofrequency low-vacuum traps with regard to the coulomb interaction

2015· article· en· W2237155666 on OpenAlexaff
I A Kopaev, Dmitry Grinfeld, Mikhail Monastyrskiy, С. С. Алимпиев

Bibliographic record

VenueJournal of Analytical Chemistry · 2015
Typearticle
Languageen
FieldChemistry
TopicMass Spectrometry Techniques and Applications
Canadian institutionsThermo Fisher Scientific (Canada)
Fundersnot available
KeywordsCoulombChemistryIonGeneralizationResolution (logic)Work (physics)Computational physicsMass spectrometryStatistical physicsAtomic physicsAlgorithmPhysicsQuantum mechanicsMathematical analysisComputer scienceChromatographyArtificial intelligence

Abstract

fetched live from OpenAlex

The paper presents main peculiarities of implementation and testing of an algorithm based on the variational approach to the problem of simulating the stationary distributions of ions in the radiofrequency, low-vacuum ion traps with taking into consideration the Coulomb interaction and interaction with buffer gas. A good agreement between the results of numerical modeling and analytical results obtained earlier by other authors for simpler models is attained. The employment of the software that has been developed in the course of this work enables studying the structure of ion ensembles in the radiofrequency ion traps of different types and obtaining the results being of interest for high-resolution mass spectrometry. The algorithm allows a natural generalization to three-dimensional case.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.020
GPT teacher head0.304
Teacher spread0.284 · 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

Citations2
Published2015
Admission routes1
Has abstractyes

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