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Record W2965990362

Infrastructure commune en acoustique pour la recherche ÉTS-IRSST

2015· article· fr· W2965990362 on OpenAlexaffvenue
Frédéric Laville, Jérémie Voix, Olivier Doutres, Cécile Le Cocq, Olivier Bouthot, Franck Sgard, Hugues Nélisse, Pierre Marcotte, Jérôme Boutin

Bibliographic record

VenueCanadian acoustics · 2015
Typearticle
Languagefr
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsInstitut de recherche Robert-Sauvé en santé et en sécurité du travailÉcole de Technologie Supérieure
Fundersnot available
KeywordsAnechoic chamberEngineeringHumanitiesAcousticsPhysicsArtTelecommunications
DOInot available

Abstract

fetched live from OpenAlex

This year, the ETS-IRSST common infrastructure for research in acoustics (ICAR) celebrates its 4th year of activity. This is a joint laboratory between the Ecole de technologie superieure (ETS) and the Institut de recherche Robert-Sauve en sante et en securite du travail (IRSST). When first created in 2011 at ETS, the lab included a semi-anechoic chamber coupled with a reverberation room. In 2014, an audiometric booth was added and a new laboratory for the characterization of acoustic materials, described in a companion paper [Doutres JCAA 2015], was added this year.

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)0.020
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.461
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
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 head0.161
GPT teacher head0.348
Teacher spread0.187 · 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.

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

Citations1
Published2015
Admission routes2
Has abstractyes

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