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Record W2508349968

Acoustical verification testing of ground run-up enclosure at Vancouver International Airport

2016· article· en· W2508349968 on OpenAlexvenueaboutno aff
Mark Bliss, Mark Cheng, Rachel Min, Ron Reeves, Ryan McMullan

Bibliographic record

VenueCanadian acoustics · 2016
Typearticle
Languageen
FieldHealth Professions
TopicNoise Effects and Management
Canadian institutionsnot available
Fundersnot available
KeywordsNoise (video)EnclosureEngineeringPlan (archaeology)Aircraft noiseNoise controlService (business)Transport engineeringInternational airportWork (physics)AeronauticsComputer scienceTelecommunicationsNoise reductionMechanical engineeringBusiness
DOInot available

Abstract

fetched live from OpenAlex

The Vancouver Airport Authority is responsible for noise management activities for aircraft arriving and departing YVR. To meet this requirement, the Airport Authority has a noise management program, and one of the main means of accomplishing the program objectives is completing initiatives contained in the YVR Noise Management Plan. As part of work on the 2009-2013 YVR Noise Management Plan, the Airport Authority decided to proceed with the design and construction of a ground run-up enclosure (GRE) to reduce noise disturbances from engine run-ups. A run-up event consists of testing engine and systems after maintenance to ensure the aircraft is airworthy and can be returned to service. Managing noise from run-ups has been a challenge given that run-ups were performed in open environments without much shielding to reduce sound propagation into the community. The construction contract included an acoustical acceptance testing requirement to determine whether the insertion loss, measured in accordance with ANSI S12.8-1998, met a minimum value of 15 dBA at the specified receiver locations. This presentation will discuss the acoustical acceptance testing that was performed for the GRE as part of the commissioning for the project.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.579
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.045
GPT teacher head0.334
Teacher spread0.289 · 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.

Study designNot applicable
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
Published2016
Admission routes2
Has abstractyes

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