Benefits and Limitations of Reliance on an Open Architecture Technical Standard to Meet Expectations of an Open System
Bibliographic record
Abstract
The US Army, Boeing, Sikorsky, Lockheed Martin and other partners conducted independent demonstrations on use of open architecture technical standards can be used to create an open system that meets the expectations identified in the Modular Open System Approach to achieve the benefits of the United States Department of Defense Better Buying Power initiative. This paper presents previously unreleased results and lessons learned from mission system experiments using the Future Airborne Capability Environment (FACETM) Technical Standard and the Army's Joint Common Architecture (JCA). The results show how to apply the FACE standard to address the expectations of an open system while identifying the potential to align with the open standard without achieving the expected benefits. This paper aims to inform acquisition authorities about the technical considerations of open architecture procurements, to provide guidance on how to apply emergent open architecture standards, and address actual or perceived gaps between meeting the requirements of an open standard and achieving the goals of procuring an open system.
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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 | 0.087 | 0.126 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".