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Record W2106322137

ÉTS-IRSST Common Infrastructure for Research in Acoustics - ICAR

2015· article· en· W2106322137 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
Languageen
FieldComputer Science
TopicImage Processing and 3D Reconstruction
Canadian institutionsInstitut de recherche Robert-Sauvé en santé et en sécurité du travailÉcole de Technologie Supérieure
Fundersnot available
KeywordsAnechoic chamberAcousticsEngineeringPhysics
DOInot available

Abstract

fetched live from OpenAlex

This year, the ÉTS-IRSST common infrastructure for research in acoustics (ICAR) celebrates its 4th year of activity. This is a joint laboratory between the École de technologie supérieure (ÉTS) and the Institut de recherche Robert-Sauvé en santé et en sécurité du travail (IRSST). When first created in 2011 at ÉTS, 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)0.001
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.844
Threshold uncertainty score0.721

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.071
GPT teacher head0.334
Teacher spread0.263 · 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 designSimulation or modeling
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
Published2015
Admission routes2
Has abstractyes

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