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Record W2117227986 · doi:10.5555/1162708.1162815

Simulation in java with SSJ

2005· article· en· W2117227986 on OpenAlexaff
Pierre L’Ecuyer, Éric Buist

Bibliographic record

VenueWinter Simulation Conference · 2005
Typearticle
Languageen
FieldMathematics
TopicMathematical Approximation and Integration
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsComputer scienceJavaRandom variateFlexibility (engineering)Set (abstract data type)Process (computing)Monte Carlo methodProgramming languageTheoretical computer scienceRandom variableStatistics

Abstract

fetched live from OpenAlex

We describe SSJ, an organized set of software tools offering general-purpose facilities for stochastic simulation programming in Java. It supports the event view, process view, continuous simulation, and arbitrary mixtures of these. Random number generators with multiple streams and sub-streams, quasi-Monte Carlo methods and their randomizations, and random variate generation for a rich selection of distributions, are all supported in an integrated framework. Performance, flexibility, and extensibility were key criteria in the design and implementation of SSJ. We illustrate its use by simple examples.

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)0.004
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.019
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0040.004
Open science0.0030.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0190.009

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 head0.097
GPT teacher head0.361
Teacher spread0.264 · 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 designSimulation or modeling
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

Citations65
Published2005
Admission routes1
Has abstractyes

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