A Systems Engineering Approach to Data Exchange Enabling Timely and Accurate Fleet Analytics and Lifecycle Management
Bibliographic record
Abstract
There is focus on the value of digital transformation over the system life cycle which is gained from seamless and efficient connectivity of data and models, full lifecycle management and access to these data and models (known as authoritative sources of truth), and an overarching imperative to radically accelerate their fielding, sustainment, and modernization of warfighter capabilities. Government mandates control of cost over all aspects and phases throughout the life cycle of a new system. However, the Departments cannot take full advantage of many of the latest advances in advanced analytic techniques in part because of limited accessibility—much of the data is siloed in specific organizations. A Condition-Based Maintenance Plus (CBM+) data share is an opportunity for data to flow in both directions across a single cohesive path using digital threads. The growing capability and complexity of modern equipment used by the Services’ deployed forces in today’s forward operations are challenging the efficacy of traditional sustainment practices. Just to name a few, Artificial Intelligence (AI) and Big Data Analytics, can provide the predictive logistics and precision sustainment functions needed by the Services and Department of Defense (DoD).
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".