5.5.3 Supportability Assessment and Evaluation During System Architecture Development
Bibliographic record
Abstract
Abstract The purpose of this technical paper is to present a framework for the evaluation of system architectures from a supportability and logistics perspective. A two‐pronged approach was implemented. The initial focus was on investigating the extent to which this issue had been addressed in the literature. Accordingly, this paper also presents a literature survey focused on the assessment and evaluation of system architectures in general, and their assessment and evaluation from a supportability perspective in particular. As part of the second thrust, leading system engineering practitioners and architects from the aerospace industry were interviewed. In this case, the objective was to synthesize the heuristic and experiential aspect of system architecture assessment and evaluation. The above two thrusts, theoretical and heuristic, led to the development of a domain independent evaluation framework represented in the form of an attribute hierarchy. The input information synthesized by this research, which lead to the evaluation framework development is presented in this technical paper. The Analytic Hierarchy Process (AHP) methodology is suggested as the preferred approach for the relative evaluation of alternative architectural approaches. Finally, extensions to this framework for increased applicability within specific domains are addressed.
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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 | 0.036 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".