Clarifying Ambiguity in Aircraft Regulatory Documentation: A Review of Modeling Approaches
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it