Bibliographic record
Abstract
The Naval Air System Command (NAVAIR) Naval Aviation Enterprise (NAE) Automated Logistics Environment (ALE) is applying Big Data Analytics and Cloud Computing Technology to support critical elements of Condition Based Maintenance Plus (CBM+) and Reliability Centered Maintenance (RCM) for NAVAIR platforms. The Comprehensive Automated Maintenance Environment -- Optimized (CAMEO) Readiness Integration Center (RIC) ALE capability focus is on the V-22 platform with an implementation strategy to apply Collaborative, Agile, Open Source, Big Data Analytics, and Cloud Technology to Collect, Connect, Warehouse, Analyze, and Act to improve platform readiness. The RIC is leveraging NAVAIR NAE ALE capability to Collect, Connect, and Warehouse critical Platform data. NAE collaboration with V-22, E2D, and Triton automated data extracts to support Platform analytic and decision support tool use and development. The RIC sponsors V-22 collaborative, agile, open source development using big data analytics and cloud technology to support the V-22 Readiness Steering Committee and Readiness Teams. The RIC actively supports Agile methodology for analytic and decision support tool development. The RIC use of Open Source tenants includes protections for intellectual property and licensing for use. The RIC development environment is consistent with Big Data and Cloud Technology computing and the RIC infrastructure and selected toolsets have completed proof of concept implementation in the Cloud. The focus of this paper is to describe a "day in the life of Big Data Analytics" from data recording and collection through RIC analytics and use by in service support engineering to effect corrective action.
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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.092 | 0.051 |
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".