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Record W2576341898 · doi:10.1155/2017/2850860

Aircraft Noise Monitoring in Function of Flight Safety and Aircraft Model Determination

2017· article· en· W2576341898 on OpenAlexvenueno aff
Andrija Vidović, Igor Štimac, Robert Zečević-Tadić

Bibliographic record

VenueJournal of Advanced Transportation · 2017
Typearticle
Languageen
FieldHealth Professions
TopicNoise Effects and Management
Canadian institutionsnot available
Fundersnot available
KeywordsAircraft noiseNoise (video)AeronauticsRadarEngineeringAerospace engineeringComputer scienceNoise reduction

Abstract

fetched live from OpenAlex

This paper presents the research of noise level monitoring at the Zagreb Airport Ltd. The purpose of this paper is to show how the aircraft noise sources can be used as a tool for aircraft detection and for increasing flight safety. The noise measurement was made at the Zagreb Airport by using its professional noise monitoring system. The research has led to new findings, such as determining the aircraft model by measuring the noise level generated by an aircraft during final approach based on its frequency spectrum, as the connectivity from the airport’s side to the radar data was unavailable (only ATC). In addition, it is possible to determine the aircraft altitude and, perhaps most significantly, the increase of flight safety through the detection of potential failures on the aircraft structure and/or engine during the overflight of a noise monitoring terminal.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.640
Threshold uncertainty score0.329

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.024
GPT teacher head0.358
Teacher spread0.334 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2017
Admission routes1
Has abstractyes

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