Next‐Generation Architecture to Support Simulation‐Based Acquisition
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT The ability to make good design decisions early is a significant driver for simulation‐based acquisition to effectively lower life‐cycle cost and cycle time. Building virtual prototypes, enabling one to analyze the impact of decisions, achieves effective simulation‐based acquisition processes. Virtual prototypes need to support a comprehensive set of analyses that will be performed on the product; hence, all aspects of product data and behavior need to be represented. Building virtual prototypes of complex systems being designed by a multi‐organizational team requires new architectural concepts and redesigned processes. Implementation of these new architectures is complex and leveraging commercial technologies is necessary to achieve feasible solutions. One must also carefully consider the state of the current commercial technologies and frameworks as well as the organizational and cultural aspects of organizations that use these systems. This paper describes key architectural principles that one must address for a cost‐effective implementation. The paper then discusses key architectural concepts and trade‐offs that are necessary to support virtual prototypes of complex systems.
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.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it