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Record W2041132750 · doi:10.5555/1400549.1400617

Experiences with the DEVStone benchmark

2008· article· en· W2041132750 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.

Bibliographic record

VenueSpring Simulation Multiconference · 2008
Typearticle
Languageen
FieldDecision Sciences
TopicSimulation Techniques and Applications
Canadian institutionsCarleton University
Fundersnot available
KeywordsBenchmark (surveying)DEVSComputer scienceModular designMetric (unit)Variety (cybernetics)Set (abstract data type)Process (computing)Modeling and simulationDiscrete event simulationDistributed computingSimulationProgramming languageArtificial intelligenceEngineering

Abstract

fetched live from OpenAlex

DEVS is a formal modeling and simulation (M&S) framework that supports hierarchical, modular model composition. DEVS-based M&S environments have been used successfully to understand, analyze, and develop a variety of systems. As the systems under study become more large and complex, performance of the simulator becomes critical. Nevertheless, evaluating the operation of such simulators is a complex process. We present DEVStone, a synthetic benchmark devoted to automate the evaluation of DEVS-based simulators, which generates models with varied structure and behavior. DEVStone was used to study the efficiency of different simulation engines provided by the CD++ toolkit, this results were later compared with similar results on ADEVS with a demanding set of experiments, enabling thorough performance analysis. DEVStone facilitates performance analysis for successive versions (e.g., upgrades or fixes) of the same simulation engine, and provides a common metric to compare different M&S environments.

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)0.001
metaresearch head score (Gemma)0.001
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.230
Threshold uncertainty score0.599

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.169
GPT teacher head0.404
Teacher spread0.236 · 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