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Record W2095897158 · doi:10.1145/2689664

DAESA—A Matlab Tool for Structural Analysis of Differential-Algebraic Equations

2015· article· en· W2095897158 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

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 Trust
KeywordsMATLABBlock (permutation group theory)MathematicsNonlinear systemAlgebraic equationDifferential (mechanical device)Code (set theory)Algebraic numberDifferential equationSet (abstract data type)AlgorithmApplied mathematicsComputer scienceMathematical analysisGeometry

Abstract

fetched live from OpenAlex

daesa , <u>D</u>ifferential-<u>A</u>lgebraic <u>E</u>quations <u>S</u>tructural <u>A</u>nalyzer, is a M atlab tool for structural analysis of differential-algebraic equations (DAEs). It allows convenient translation of a DAE system into M atlab 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 also can construct a block-triangular form of the DAE, which can be exploited to solve it efficiently in a block-wise manner. This article describes the theory and algorithms underlying the code.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Methods · Consensus signal: none
Teacher disagreement score0.636
Threshold uncertainty score0.486

Codex and Gemma teacher scores by category

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

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 head0.068
GPT teacher head0.311
Teacher spread0.243 · 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