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8.4.2 Exploring Concurrent Activities: Using State Machines to Understand Net‐Enabled Operations

2007· article· en· W1558014782 on OpenAlexaboutno aff
Richard L. Sorensen, Ronald Funk, Mark G. Ball

Bibliographic record

VenueINCOSE International Symposium · 2007
Typearticle
Languageen
FieldDecision Sciences
TopicSimulation Techniques and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceExploitWorkflowTask (project management)Process (computing)DisseminationSample (material)State (computer science)Software engineeringIndustrial engineeringSystems engineeringDatabaseEngineeringComputer securityOperating systemAlgorithm

Abstract

fetched live from OpenAlex

Abstract The Defence Research and Development Canada (DRDC) Centre for Operational Research and Analysis (CORA) is developing capability engineering analysis tools to help build and assess architectures to support net‐enabled operations. This paper describes research into State‐Machine (SM) models to simulate job workflows that are typically done serially using the Task, Process, Exploit and Disseminate (TPED) cycle or concurrently using the Task, Post, Process, Use (TPPU) cycle. Classical behavioural models cannot simulate TPPU beyond simple cases due to all the possible permutations in the job flow. SM models overcome this by storing the status of activities and products and then using them in the next time step as inputs to change the status through an action or an output. The SM approach shifts the modeling perspective to an instant in time so the interrelationships of concurrent activity can be simulated in more detail than is feasible for classical architecture models. Sample results and analysis tools for the current SM model are presented.

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 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)0.000
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 score0.392
Threshold uncertainty score0.596

Codex and Gemma teacher scores by category

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

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 head0.285
GPT teacher head0.449
Teacher spread0.164 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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

Citations0
Published2007
Admission routes1
Has abstractyes

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