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Record W2007194272 · doi:10.1145/2700586

Algorithm 948

2015· article· en· W2007194272 on OpenAlexafffund
Nedialko S. Nedialkov, John D. Pryce, Guangning Tan

Bibliographic record

VenueACM Transactions on Mathematical Software · 2015
Typearticle
Languageen
FieldComputer Science
TopicModeling and Simulation Systems
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaLeverhulme TrustMcMaster University
KeywordsMATLABBlock (permutation group theory)Algebraic equationNonlinear systemComputer scienceDegrees of freedom (physics and chemistry)Differential algebraic equationSet (abstract data type)AlgorithmScheme (mathematics)Translation (biology)Differential equationDifferential (mechanical device)MathematicsOrdinary differential equationMathematical analysisGeometry

Abstract

fetched live from OpenAlex

DAESA, Differential-Algebraic Equations Structural Analyzer, is a Matlab tool for structural analysis of differential-algebraic equations (DAEs). It allows convenient translation of a DAE system into Matlab and provides a small set of easy-to-use functions. DAESA can analyze systems that are fully nonlinear, high-index, and of any order. It determines structural index, number of degrees of freedom, constraints, variables to be initialized, and suggests a solution scheme. The structure of a DAE can be readily visualized by this tool. It can also construct a block-triangular form of the DAE, which can be exploited to solve it efficiently in a block-wise manner.

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)0.006
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: Software · Consensus signal: none
Teacher disagreement score0.109
Threshold uncertainty score0.363

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.1090.040

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 head0.070
GPT teacher head0.292
Teacher spread0.222 · 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
GenreSoftware

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

Citations16
Published2015
Admission routes2
Has abstractyes

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