5.5.3 Enabling Systems Architecture Tradeoffs Using a Systems Integration Framework
Bibliographic record
Abstract
Abstract Systems integration of large information enterprises involves integrating multiple heterogeneous database systems, client‐server platforms, web application technologies, and data warehousing solutions. Lockheed Martin Management and Data Systems (M&DS) is involved in systems integration for state, local, and federal government customers. We are currently developing a Systems Integration Framework (SIF) to assist in the architecture tradeoff and selection for large information enterprises. A SIF is an environment, which will assist in the rapid prototyping and testing of architectures, and specific instantiations of the architectures using commercial tools and platforms. Over the last four years we have developed and tested architectures for client‐server applications, web applications, mobile computing applications, data warehouses, and data mining applications. Performance metrics have been collected for web application servers and client‐server applications. This paper will describe how we utilized the SIF as a rapid prototyping and tradeoff environment to support systems engineering for state government customers in domains such as law enforcement, criminal justice, and airline operations.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".