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Record W2924415364 · doi:10.1049/pbpc018e_ch1

Modeling and simulation: the essence and increasing importance

2019· book-chapter· en· W2924415364 on OpenAlexaff
Tuncer Ören, Saurabh Mittal, Umut Durak

Bibliographic record

VenueInstitution of Engineering and Technology eBooks · 2019
Typebook-chapter
Languageen
FieldDecision Sciences
TopicSimulation Techniques and Applications
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsComputer sciencePerceptionOntologyEntertainmentVariable (mathematics)Management scienceHuman–computer interactionEpistemologyEngineeringMathematicsPolitical science

Abstract

fetched live from OpenAlex

The technical aspects of the essence of simulation are elaborated based on the following definition: simulation is performing a goal-directed experimentation or gaining experience under controlled conditions by using dynamic models either to develop/enhance skills or for entertainment; where a dynamic model denotes a model for which behaviour and/or structure is variable over time. Hence, experimentation and experience aspects are explained. Several taxonomies, ontologies, and some ontology-based dictionaries are cited for a comprehensive and integrative perception of simulation. Finally, the evolution and increasing importance of simulation is explained.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0010.005
Scholarly communication0.0060.006
Open science0.0010.002
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0100.004

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 head0.044
GPT teacher head0.306
Teacher spread0.263 · 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 designTheoretical or conceptual
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

Citations2
Published2019
Admission routes1
Has abstractyes

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