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Record W2033094471 · doi:10.1121/1.4805554

The effect of the shadowing phenomenon on emergency vehicle siren noise

2013· article· en· W2033094471 on OpenAlexaff
Peter D’Angela, Frankie Angione, Colin Novak, Helen Ule

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2013
Typearticle
Languageen
FieldHealth Professions
TopicNoise Effects and Management
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsSiren (mythology)Emergency vehicleNoise (video)AeronauticsFrench hornAcousticsAutomotive engineeringEnvironmental scienceComputer scienceSimulationEngineeringTelecommunicationsPhysicsHistoryArtificial intelligence

Abstract

fetched live from OpenAlex

It has been observed by some that emergency siren noise has gone unnoticed by drivers due to a shadowing phenomenon where the propagating siren noise is blocked from a receiver vehicle. The event is postulated to occur when a large vehicle is positioned between the emergency responder and a receiving vehicle. The sound of the siren is projected along the surface of the large vehicle and does not fall in time to reach the receiving vehicle. This situation is common at controlled intersections where the smaller vehicle is traveling perpendicular to the emergency vehicle but can also occur when the vehicles are in a common line on the road. The intent of this study is to investigate this phenomenon and quantify the resulting hindrance of a driver’s ability to detect an approaching emergency vehicle. Included will be the use of the electrical “wail” siren and accompanying air horn commonly employed by Fire and Rescue Services. The outcome will be a determination of what frequency spectra are most affected by shadowing with an eventual goal to improve emergency siren design.

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)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.645
Threshold uncertainty score0.899

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.013
GPT teacher head0.320
Teacher spread0.307 · 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 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

Citations2
Published2013
Admission routes1
Has abstractyes

Explore more

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