High‐Level Methodologies to Evaluate Naval Task Groups
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Defense organizations within many nations (e.g., United States and Canada), use capability‐based planning (CBP) to guide their force development processes. A key element of the CBP process is testing current and proposed capabilities against force planning scenarios, particularly for asset evaluation. This analysis involves a wide range of capabilities, and thus is a multicriteria problem. Comparison of alternatives using multiple criteria is challenging, and often is assisted by aggregation techniques. Set in a naval context, this paper presents three high‐level capability aggregation techniques: the vector method, star plot method, and wedge method. Each method aggregates naval task group capabilities, with respect to a scenario, into three quantifiable measures: effectiveness, unmatched, and unused. As with numerous techniques, the effectiveness gauges the ability of a task group to meet a set of scenario requirements. The unmatched and unused measures yield insight into capability gaps, which is an important aspect of CBP. The unmatched metric measures scenario requirements that are not provided by a task group and the unused metric measures task group capabilities that are not required by a scenario. An application of the methods is presented, including a discussion of their strengths and weaknesses. Based on this work, it is concluded that the vector method is the best of the three presented.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it