State Machine of Federated Nodes By
Bibliographic record
Abstract
Research and Analysis (CORA) is developing capability-engineering analysis tools to support the building, demonstration, and analysis of executable architectures. Our paper to 11th ICCTS [1] described how to model workflows within an Operations Centre (OPCEN) employing a Net-Centric architecture. It used a State Machine (SM) model to simulate how multiple jobs can proceed in parallel when operators use Task, Post, Process, Use (TPPU) cycle to organize their work. This paper extends the OPCEN SM model to track the interaction of work between OPCENs. The State Machine of Federated Nodes (SMOFN) model is organized around networked nodes that produce and consume products held in a virtual Repository. The data-driven simulation uses files to build customized job workflows and configure any combination of nodes without affecting the business logic. SMOFN also accounts for the following overhead activities: (1) Tracking consumer perception of product utility as it accrues and decays; (2) Consolidation of products into higher-level aggregated products; and (3) Triggering new jobs where needed whenever relevant products become available. Customization of SMOFN is underway to account for the data and product flows between OPCENs in new Canadian Forces Command structure.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".