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Record W2131363101 · doi:10.1515/jnma-2024-0137

The deal.II library, Version 9.6

2024· article· en· W2131363101 on OpenAlexaff
Pasquale Claudio Africa, Daniel Arndt, Wolfgang Bangerth, Bruno Blais, Marc Fehling, René Gassmöller, Luca Heltai, Sebastian Kinnewig, Martin Kronbichler, Matthias Maier, Peter Münch, Magdalena Schreter-Fleischhacker, Jan Philipp Thiele, Bruno Turcksin, David Wells, Vladimir Yushutin

Bibliographic record

VenueJournal of Numerical Mathematics · 2024
Typearticle
Languageen
FieldEngineering
TopicAdvanced Numerical Methods in Computational Mathematics
Canadian institutionsPolytechnique Montréal
FundersDepartment of Mechanical Engineering, University of Texas at AustinSan Diego Supercomputer CenterUniversity of Texas at AustinOffice of Advanced CyberinfrastructureUniversity of California, San DiegoNational Science Foundation
KeywordsFinite element methodMathematicsLibrary scienceComputer scienceEngineeringStructural engineering

Abstract

fetched live from OpenAlex

Abstract This paper provides an overview of the new features of the finite element library deal.II, version 9.6.

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)0.002
metaresearch head score (Gemma)0.009
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: Not applicable
GenreCandidate signal: Software · Consensus signal: none
Teacher disagreement score0.211
Threshold uncertainty score0.705

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0020.003
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.002
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0060.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.2110.207

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 head0.015
GPT teacher head0.283
Teacher spread0.268 · 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

Citations97
Published2024
Admission routes1
Has abstractyes

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