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Record W2579801605 · doi:10.5555/3042094.3042116

A tutorial on ABCmod: an activity based discrete event conceptual modelling framework

2016· article· en· W2579801605 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

VenueWinter Simulation Conference · 2016
Typearticle
Languageen
FieldDecision Sciences
TopicSimulation Techniques and Applications
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsComputer scienceConceptual modelDomain (mathematical analysis)Domain modelConceptual frameworkProcess (computing)Event (particle physics)Perspective (graphical)NaturalnessDiscrete event simulationArtificial intelligenceProgramming languageSimulationMathematicsEpistemology

Abstract

fetched live from OpenAlex

The notion of a conceptual model is present in any discussion about the modelling and simulation process within the discrete event dynamic system domain (Robinson 2011). This paper presents an overview on an activity-based conceptual modelling framework: Activity Based Conceptual modelling = ABCmod (Birta and Arbez 2013). It transforms the general notion of a conceptual model to into a specific conceptual modelling artefact. The ABCmod framework encompasses the naturalness of the activity perspective which has considerable intuitive appeal (Pidd 2004a and 2004b). ABCmod accommodates both the structural and the behavioral aspects that are fundamental components of any conceptual model and provides a collection of constructs both for handling input/output and for dealing with special circumstances such as pre-emption, interruption and balking. We provide an overview of the framework and illustrate many of its features in 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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.808
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.259
GPT teacher head0.459
Teacher spread0.200 · 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