MétaCan
Menu
Back to cohort
Record W2107536167 · doi:10.1109/pac.2007.4440380

Runge-kutta da integrator in mathematica language

2007· article· en· W2107536167 on OpenAlexaff
D. Kaltchev, R. Baartman

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMathematics
TopicNumerical methods for differential equations
Canadian institutionsTRIUMF
Fundersnot available
KeywordsIntegratorPower seriesEquations of motionDifferential equationSolverSymbolic computationMotion (physics)Runge–Kutta methodsComputer scienceAlgebra over a fieldSymplectic geometryCode (set theory)Series (stratigraphy)MathematicsMathematical analysisPhysicsClassical mechanicsPure mathematicsArtificial intelligence

Abstract

fetched live from OpenAlex

The method of truncated power series algebra is applied in a Mathematica code to compute the transfer map for arbitrary equations of motion describing a charged particle optical system. The code is a non-symplectic integrator - a combination between differential algebra module and a numerical solver of the equations of motion. Using the symbolic system offers some advantages, especially in the case of non-autonomous equations of motion (element with fringe-fields). An example is given - a soft-fringe map of a magnetic quadrupole.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.013
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0130.007

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 head0.080
GPT teacher head0.426
Teacher spread0.346 · 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 designNot applicable
Domainnot available
GenreMethods

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

Explore more

Same topicNumerical methods for differential equationsFrench-language works237,207