Applying a FEDEP VV&A overlay to the MALO project using the REVVA VV&A process
Bibliographic record
Abstract
The Maritime Air Littoral Operations (MALO) technology demonstration project, conducted by Defence RD for client acceptance it was critical that the MALO system be able to conduct doctrine and tactics analysis using accurate representations of existing and potential maritime systems and scenario.The NATO M&S Group in cooperation with the Simulation Interoperability Standards Organization (SISO) created a VV&A overlay for the FEDEP, and a common VV&A process for Modelling and Simulation (M&S) was created by the European REWA government and industry consortium. MALO VV&A was conducted using an amalgamation of these processes, including creating Target of Acceptance and Target of V&V diagrams, creation of a V&V plan, and tracking of the results, using the Assurance and Safety Case Environment (ASCE software).This paper reports the planning and results of the MALO VV&A, primarily from the perspective of the effectiveness of the NATO/SISO VV&A and REWA processes and ASCE software. The paper includes practical advice on the conduct of W&A using the processes, overview of the functionality and limitations of ASCE, lessons learned, and further development recommendations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".