8.4.2 Exploring Concurrent Activities: Using State Machines to Understand Net‐Enabled Operations
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".