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Record W1937221909

Experimental validation of the objective measurement of individual custom earplug field performance

2006· article· en· W1937221909 on OpenAlexfundvenueno aff
Jérémie Voix, Lee D. Hager, Jean Zeidan

Bibliographic record

VenueCanadian acoustics · 2006
Typearticle
Languageen
FieldDecision Sciences
TopicScientific Measurement and Uncertainty Evaluation
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAttenuationOctave (electronics)AcousticsField (mathematics)Octave bandComputer scienceOpticsMathematicsPhysics
DOInot available

Abstract

fetched live from OpenAlex

A method was developed to meet the need to improve earplug field performance prediction accuracy by changing from average group performance prediction to individual performance prediction. The individual earplug field performance was objectively measured, using the Field-MIRE method. The individual attenuation was predicted from the field measurement of the Noise Reduction through the earplug. The individual attenuation was first obtained as a set of values for each octave band center frequency and these values were combined in a single number, the Predicted Personal Attenuation Rating (P-PAR). The observation confirmed that the low-frequency octave-band attenuation could be a good predictor of the overall attenuation as most earplugs attenuate significantly in high frequency. The observation also suggested that the high-frequency attenuation prediction adds much information and appeared to disturb the prediction capabilities of the measurement system.

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)0.004
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.129
GPT teacher head0.325
Teacher spread0.196 · 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 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

Citations6
Published2006
Admission routes2
Has abstractyes

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