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Record W1516627515

Propagation of sound behind vehicles equipped with different backup alarms

2011· article· en· W1516627515 on OpenAlexafffundvenue
Hugues Nélisse, Chantal Laroche, Jérôme Boutin, Christian Giguère, Véronique Vaillancourt

Bibliographic record

VenueCanadian acoustics · 2011
Typearticle
Languageen
FieldEngineering
TopicVehicle Noise and Vibration Control
Canadian institutionsUniversity of OttawaInstitut de recherche Robert-Sauvé en santé et en sécurité du travail
FundersInstitut de Recherche Robert-Sauvé en Santé et en Sécurité du Travail
KeywordsBackupALARMAcousticsEnergy (signal processing)Sound pressureBroadbandSound energyTone (literature)Range (aeronautics)EngineeringComputer scienceSound (geography)Real-time computingTelecommunicationsElectrical engineeringPhysics
DOInot available

Abstract

fetched live from OpenAlex

A study that focuses on sound propagation behind vehicles by examining the distribution of sound pressure levels for three types of backup alarms is presented. The three types of backup alarms include the standard tonal 'beep' signal, a multi-tone signal and the broadband noise technology. Sound pressure levels were measured at various fixed locations behind heavy vehicles by using a test method inspired from the ISO 9533 standard. The multi-tone alarm consists of three major tones between 1000 and 1300 Hz. in contrast to the standard tonal alarm with its acoustic energy concentrated around 1250 Hz. For the broadband alarm, energy is distributed over a larger frequency span, most of the energy being found in the 700-4000 Hz range. The alarm levels were set at the values found during the first set of measurements and the vehicle engine was stopped. A post-processing scheme was developed to extract the alarms' sound pressure levels along the various lines.

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)0.000
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.017
GPT teacher head0.184
Teacher spread0.167 · 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

Citations1
Published2011
Admission routes3
Has abstractyes

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