Bibliographic record
Abstract
Aircraft noise is one of the most controversial environmental concerns in the aviation industry, partly due to the difficulty in harmonizing countries' regulation regarding this issue. The purpose of this thesis is to analyze the ways in which aircraft noise is regulated at the national and international levels, and to compare the legislative responses to aircraft noise issues in Europe and North America. Each of the four main chapters of the thesis takes into consideration a different aspect of the problem. The first chapter describes the objective and subjective ways of measuring aircraft noise. This process is necessary in order to allow the legislation to meet its purpose, namely, to protect the environment, the sources of the aircraft noise, and the effects of the aircraft noise on people. The second chapter describes the evolution of aircraft noise issues at the national levels in the US and throughout the EU, respectively, as well as at the international level, such as at the ICAO. The third chapter analyses the EU Regulation 925/1999, which created tension between the EU and the US due to its alleged discriminatory nature. This thesis examines the arguments of both sides. Finally, the fourth chapter analyses the noise certification standards developed by ICAO, namely the "balanced approach".
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.449 | 0.273 |
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".