Bibliographic record
Abstract
This paper discusses the correlation of IVHM (Integrated Vehicle Health Management) as an emerging aerospace discipline and the Big Data paradigm widely discussed in the Information Technology industry. The 4-V model is discussed to qualify a Big Data problem in terms of the volume, variety, velocity and veracity of the data involved. Big Data management allows, for example, correlations to be found to “spot business trends, determine quality of research, prevent diseases, combat crime, and determine real-time roadway traffic conditions”. Examining these two fields side by side is necessary and desirable because innovation is very likely to occur when and where different but correlated domains interface. This paper compares the most significant technical components required for Big Data Analytics and IVHM to work. Most aerospace players have recognized the strategic value of IVHM, but only a few have been able to establish coordinated action plans that allow them to offer advanced services in line with the possibilities envisioned. To help address this fact, the paper will discuss the challenges for IVHM implementation, including how the aircraft design process needs to be modified to incorporate IVHM trade studies complete enough to allow the enterprise to compare development costs to long term savings on operations, maintenance and sustaining efforts. A generic IVHM platform will be described, including examples for the ground analytics components, which are identified as the most value added parts.
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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 | 0.022 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".