FEDspresso - CAFDE Based HLA Federation Development and Implementation Tool Suite
Bibliographic record
Abstract
Report developed under SBIR contract. During the Phase II effort, Synetics developed the OMSuite(TM) HLA Federation Development Tools - OMCase(TM), OMBuilder(TM), OMSpector(TM), OMLex(TM), OMNet(TM), and OMManager(TM). The OMSuite(TM) tool suite provides an integrated tool environment, connected via the Computer Aided Federation Development Environment (CAFDE) Engine allowing for the integration of third party components, support for on-line collaboration, and assistance in one or more aspects of federation engineering: requirements generation, federation construction, and execution transition. The major emphasis was placed on the development of OMCase(TM) for requirements capturing, OMBuilder(TM) for object modeling definition, and OMSpector(TM) for providing federation adaptability. The OMSuite(TM) tool suite has been sold to several U.S. companies and to the Canadian government. Additionally, OMSuite(TM) is being used in the development of the large-scale simulation at the TEAMs facility at NSWCDD, Dahlgren, Virginia. The Synetics OMSuite(TM) development team has also published and presented many papers for the Simulation Interoperability Workshops, contributed to refining of FEDEP processes, and assisted in leading the Base Object Models (BOMs) methodology effort within the SISO community.
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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.011 |
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".