Considerations for Requirements and Specifications of a Digital Thread in Aircraft Data Life Cycle Management
Bibliographic record
Abstract
The aircraft lifecycle involves thousands of transactions and an enormous amount of data being exchanged across the stakeholders in the aircraft ecosystem. This data pertains to various aircraft life cycle stages such as design, manufacturing, certification, operations, maintenance, and disposal of the aircraft. All participants in the aerospace ecosystem want to leverage the data to deliver insight and add value to their customers through existing and new services while protecting their own intellectual property. The exchange of data between stakeholders in the ecosystem is involved and growing exponentially. This necessitates the need for standards on data interoperability to support efficient maintenance, logistics, operations, and design improvements for both commercial and military aircraft ecosystems. A digital thread defines an approach and a system which connects the data flows and represents a holistic view of an asset data across its lifecycle. The digital thread framework addresses data flow procedures, data security, and data governance aspect. The digital thread includes as-designed requirements, validation and inspection records, certification information, as-manufactured data, as-operated data, and as-maintained data. This paper discusses the considerations for requirements, specifications, and framework of a digital thread in aircraft data lifecycle management.
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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 | 0.063 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.020 | 0.019 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".