In-cabin noise levels during commercial aircraft flights
Bibliographic record
Abstract
Air transport is one of the most commonly used mode of transportation and hence passenger comfort is highly desirable. Aircraft interior noise is important, especially in long-term flights, concerning the health, comfort, and psychological wellness of both passengers and flight crew. Noise levels, which changes according to different motions of aircraft, can be defined as the noise during takeoff and landing and during level flight (cruise). There are also non-aircraft-originating noise sources in the cabin. These can be classified into those caused by passenger activities such as conversations and luggage-related rearrangements as well as those caused by flight-crew such as flight attendant-related speaking activities, announcements from pilot and flight attendants, mechanical noises during food/beverage services and flight security demonstrations, and other announcement signals. In this study, in-cabin noise levels were measured during all flight activities in a commercial jet passenger plane. These noise levels consist of both continuous and discontinuous types. As a general tendency, continuous noise levels were seen to be 60-65 dB(A) prior to takeoff, and 80-85 dB(A) and 75-80 dB(A) during flight and landing, respectively. Discontinuous in-cabin noise levels were observed to reach levels as high as 81-88 dB(A) range. This study shows that it can be possible to control and reduce in-cabin noise levels, especially due to human activities and a few recommendations are suggested.
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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 | 0.000 | 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) | 0.001 | 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 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".