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Record W1600511398

Nose conedevice and built-in calibration check as essenti al features of a standalone instrument for unattended mid- And long-term noise measurements

2012· article· en· W1600511398 on OpenAlexvenueno aff
Daniel Vaucher de la Croix, Erik Aflalo

Bibliographic record

VenueCanadian acoustics · 2012
Typearticle
Languageen
FieldEngineering
TopicIndustrial and Mining Safety
Canadian institutionsnot available
Fundersnot available
KeywordsMicrophoneNoise (video)Sound level meterAcousticsCalibrationMetreAmbient noise levelEnvironmental noiseEngineeringTerm (time)Noise measurementBackground noiseComputer scienceNoise levelSound (geography)Sound pressurePhysicsNoise reductionArtificial intelligence
DOInot available

Abstract

fetched live from OpenAlex

The article examines how the nose cone device and built-in calibration check are essential features of a stand alone instrument for unattended mid- and long-term noise measurements. Environmental noise generated by ground transportation, construction sites or recreational activities, is coming from all directions, and such multiple sources are usually located at random positions with respect to the measurement point. JEC 61672-1 gives directional response requirements for the configuration of a sound level meter as stated in the instruction manual for the normal mode of operation or for those components of a sound level meter that are intended to be located in a sound field. In unattended noise measurement situations, the direction from the source is generally unknown. Apart from aircraft noise, the sources are located on the ground. Taking into account the acoustic front end design at the early stage of the development allowed determining and optimizing the constraints: position of the microphone, shape of the body of the sound level meter, cone and wind screens characteristics.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.583
Threshold uncertainty score0.519

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 head0.033
GPT teacher head0.255
Teacher spread0.222 · 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 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
Published2012
Admission routes1
Has abstractyes

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