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Record W1548219921

CartaBlanca-rapid prototyping development environment for non-linear systems on unstructured grids.

2002· paratext· en· W1548219921 on OpenAlexaff
W. B. VanderHeyden, Daniel Livescu, N. T. Padial‐Collins

Bibliographic record

VenueUniversity of North Texas Digital Library (University of North Texas) · 2002
Typeparatext
Languageen
FieldComputer Science
TopicDistributed and Parallel Computing Systems
Canadian institutionsNexen (Canada)
FundersLos Alamos National LaboratoryU.S. Department of Energy
KeywordsComputer scienceScalaPython (programming language)Software developmentSolverSoftwareJavaComputational scienceComponent-based software engineeringProgramming language
DOInot available

Abstract

fetched live from OpenAlex

This talk describes a component-based nonlinear physical system simulation prototyping package written entirely in Java using objectoriented design, The package provides scientists and engineers a 'developer-friendly' software environment for large-scale computational algorithm and physical model development, on the Jacobian-Free Newton-Krylov solution method surrounding a finite-volume treatment of conservation equations. This enables a clean component-like implementation. We first provide motivation for the development of the software and then discuss software structure. Discussion .includes a description of the use of Java's built-in thread facility that enables parallel, shared-memory computations on a wide variety of unstructured grids with triangular, quadrilateral, tetrahedral and hexahedral elements. We also discuss the use of Java's inheritance mechanism in the construction of a hierarchy of physics systems objects and linear and nonlinear solver objects that simplify development and foster software re-use. Following this, we show results from example calculations and then discuss plans including the extension of the software to distributed memory computer systems.

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 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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.488
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0040.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.014
GPT teacher head0.171
Teacher spread0.157 · 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 teacher head, not a consensus.

Study designNot applicable
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

Citations0
Published2002
Admission routes1
Has abstractyes

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