Clarifying Ambiguity in Aircraft Regulatory Documentation: A Review of Modeling Approaches
Bibliographic record
Abstract
Aircraft design and development programs must comply with many certification requirements described in natural language provided in a complex collection of document-based regulations and associated guidance material. As a result, individual design organizations develop internal processes detailing how regulatory requirements should be met within their aircraft design programs. Subject matter experts develop these processes, which are subjective interpretations of the regulations and can vary significantly between development programs and organizations. Model-based approaches are increasingly used to manage the complexity of the aircraft design and development process. Regulatory documentation, however, remains document-based, making certification a costly component of the design process. This paper reviews three approaches to modeling regulatory documentation of process mapping, ontological modeling, and Unified Modeling Language (UML) and compares their utility in the context of reducing ambiguity, reflecting complexity, and leveraging subject matter expertise. A case study is presented using the advisory circular AC 21.101-1B Establishing the Certification Basis of Changed Aeronautical Products to illustrate the comparison.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".