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Record W4389273124 · doi:10.3397/in_2023_0305

Characteristics of community responses to airport noise around Bangkok International Airport

2023· article· en· W4389273124 on OpenAlexaff
Krittika Lertsawat, Ichiro Yamada, Takashi Yano, Rattapon Onchang, Satanat Kitsiranuwat, Thapana Boonchoo, Alongkorn Pimpin, Supet Jirakajohnkool

Bibliographic record

VenueNOISE-CON proceedings · 2023
Typearticle
Languageen
FieldHealth Professions
TopicNoise Effects and Management
Canadian institutionsImpact
Fundersnot available
KeywordsRunwayInternational airportASDE-XNoise (video)Aircraft noiseAir traffic controlTransport engineeringNoise controlBaseline (sea)Traffic noisePlan (archaeology)EngineeringAeronauticsGeographyComputer scienceNoise reductionPolitical science

Abstract

fetched live from OpenAlex

A social noise survey is a study of the community's response to noise. Data from a 2014-2015 noise survey of communities living around Bangkok International Airport is considered a spatial study that is important for the long-term planning of the airport development plan to prevent noise problems from aircraft operations. This paper presents the nature of survey data on airport noise from people living around Suvarnabhumi Airport over a decade after the airport's opening in 2006. It is now planned to expand the airport's capacity to handle more air traffic volume through runway expansion and passenger terminal expansion. The cross-sectional study is thought to be useful in future airport noise planning to avoid severe aircraft noise problems. In addition, it was an appropriate time to collect data from the field as a baseline for future airport noise management before expanding the capacity to handle more air traffic by adding more runways and passenger terminals.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.034
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.073
GPT teacher head0.399
Teacher spread0.326 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2023
Admission routes1
Has abstractyes

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