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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 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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.686
Threshold uncertainty score0.653

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.017
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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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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