Creation of a system taxonomy for advanced air mobility operations
Bibliographic record
Abstract
Advanced air mobility (AAM) is an emerging field that envisions highly automated, high-tempo, passenger-carrying aircraft operating in urban environments. Numerous stakeholders have emerged in this space, each proposing unique concepts of operation (CONOPS). While this technology will enable a revolutionary leap in modern transportation, many challenges remain unaddressed. Discrepancies exist between envisioned AAM operations, current technology, and outdated federal regulations. This has led to an unclear vision within the industry of the current maturity level of technology and the path of development needed to achieve proposed CONOPS. We seek to fill this gap by integrating multiple proposed conceptual systems into a single, unified path forward for the field. The literature on AAM covers a diverse array of interconnected themes, each containing a multitude of considerations that require extensive exploration and understanding. In the present work, a literature integration was conducted to collate the numerous proposed concepts and envisioned works that exist within the AAM literature. With this information, a comprehensive AAM taxonomy was created to represent this integration of concepts and rectify the inconsistencies within industry CONOPS. This taxonomy will assist future stakeholders in navigating this intricate field while contributing to the safe and efficient development and deployment of AAM.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".