Noise Estimation Framework for Advanced Air Mobility
Bibliographic record
Abstract
Community acceptance and adoption has been identified as a critical and challenging component as we prepare our communities for Advanced Air Mobility (AAM) operations.Although AAM vehicles may be significantly quieter than traditional rotorcraft, the proposed scale of operations and proximity to the built environment makes noise generated from AAM operations a concerning element.Therefore, it is essential to incorporate noise estimation in the planning stage.The presented analysis demonstrates a noise estimation framework for AAM operations.The framework utilizes simulation models and the current state of knowledge combined with laboratory data to inform the modeled sound sources.This framework is part of the Advanced Air Mobility -Community Integration Planning Tool.The noise estimation framework takes trajectories of proposed AAM operations and simulates them with the Advanced Acoustic Model to compute their noise exposure.Combined with demographics, the results can assess the potential for noise impact of the proposed operations.The framework is developed to provide quick and credible noise results with the ability to vary temporal and spatial granularity while accommodating different types of ---------
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".