6.4.2 A Metric Framework for Capability Definition, Engineering and Management
Bibliographic record
Abstract
Abstract As defence planning and management evolves from a platform‐centric, threat‐based approach toward a capability‐based paradigm, the need for a rigorous approach to systems engineering at the capability level is amplified. This is because capability‐based plans incorporate system‐of‐systems configurations with varying developmental timeframes that must deliver interoperable effects on the battlefield. In addressing this challenge, a capability‐based planning construct is being examined within the Department of National Defence. This construct is supported by integrating and enabling concepts like enterprise architectures, system‐of‐systems engineering principles and capability metrics. While an architecture framework is useful for developing functional requirements of a capability, a metric framework, as this paper contends, can be used as a guide for defining and articulating desired quality characteristics. This paper describes the concept of a capability metric framework, and how it has been applied to define capability goals and evaluate implementation options.
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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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".