Assessing Systems of Systems’ Performance Using a Hierarchical Evaluation Process
Bibliographic record
Abstract
As the course and direction of military conflicts continue to change at the strategic, operational and tactical levels, decision makers need to assess their capability more rapidly, with higher levels of trust, fidelity and effectiveness.Decision makers receive a large number of proposals or acquisition requests for new or upgrading systems, processing and related infrastructure improvements.They review, evaluate and assess the relative and absolute effectiveness of each proposal and mitigate any risk factors associated with each potential acquisition and the integration of additional capabilities to the overall system.The capabilities can be broken down into performance, integration and interoperability of system of systems in order to achieve ultimate performance to address their mission requirements.This paper will present the design of a decision support tool called the Virtual Intelligence, Surveillance and Reconnaissance (ISR) Evaluation Environment (VIEE) and the methodology that is used for the overall performance evaluation of different ISR system of systems based on Analytical Hierarchy Process (AHP) and Analytical Network Process (ANP) methods.A scenario will be used to describe how the tools function in VIEE while demonstrating the effectiveness of system of systems performance.
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 | 0.046 | 0.055 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".