Federation Development and Execution Process (FEDEP) Tools in Support of NATO Modelling & Simulation (M&S) Programmes (Des outils d'aide au processus de developpement et d'execution de federations (FEDEP)) (CD-ROM)
Bibliographic record
Abstract
ELECTRONIC FILE CHARACTERISTICS: 142 files; HyperText Markup Language (.HTML) and Adobe Acrobat (.PDF). PHYSICAL DESCRIPTION: 1 computer laser optical disc (CD-ROM); 4 3/4 in.; 204 MB. SYSTEMS DETAIL NOTE: Adobe Acrobat Reader is included on disc. ABSTRACT: This report details the work and activities undertaken by the NATO MSG-005 technical activity programme "Federation Development and Execution Process (FEDEP) Tools in Support of NATO Modelling & Simulation (M&S) Programmes". Specifically, it provides the methodology and rational used to develop a database of tools that support the creation of HLA federations in NATO. The nations involved in this study were Canada, France, Germany, Portugal, United Kingdom, and the United States. The HLA Tool database was compiled in a web-accessible format. The web interface was created by Germany and Canada has volunteered to host the database until a permanent NATO M&S repository can be created.
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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".