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Record W1965632828 · doi:10.1016/j.procs.2010.04.166

Object construction and destruction design patterns in Fortran 2003

2010· article· en· W1965632828 on OpenAlexaff
Damian Rouson, Jim Xia, Xiaofeng Xu

Bibliographic record

VenueProcedia Computer Science · 2010
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Heat Transfer
Canadian institutionsIBM (Canada)
FundersOffice of Naval ResearchNational Nuclear Security AdministrationInternational Business Machines CorporationSandia National LaboratoriesU.S. Department of Energy
KeywordsComputer scienceFortranLeverage (statistics)Programming languageSoftware design patternFactory (object-oriented programming)Software engineeringPerspective (graphical)Object-oriented programmingSoftwareTheoretical computer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

This paper presents object-oriented design patterns in the context of object construction and destruction. The examples leverage the newly supported object-oriented features of Fortran 2003. We describe from the client perspective two patterns articulated by Gamma et al. [1]: abstract factory and factory method. We also describe from the implementation perspective one new pattern: the object pattern. We apply the Gamma et al. patterns to solve a partial differential equation, and we discuss applying the new pattern to a quantum vortex dynamics code. Finally, we address consequences and describe the use of the patterns in two open-source software projects: ForTrilinos and Morfeus.

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)0.003
metaresearch head score (Gemma)0.008
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 score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0030.003
Open science0.0020.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.003

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 head0.006
GPT teacher head0.188
Teacher spread0.182 · 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

Citations10
Published2010
Admission routes1
Has abstractyes

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