MétaCan
Menu
Back to cohort
Record W2626354448 · doi:10.1121/1.4987243

Apparent sound insulation in cross-laminated timber buildings

2017· article· en· W2626354448 on OpenAlexaffabout
Christoph Hoeller, Jeffrey Mahn, David Quirt

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2017
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsSoundproofingSound transmission classCross laminated timberSound (geography)Transmission (telecommunications)Forensic engineeringEngineeringComputer scienceArchitectural engineeringStructural engineeringAcousticsCivil engineeringTelecommunications

Abstract

fetched live from OpenAlex

With the 2015 National Building Code now in effect in Canada, predicting the apparent sound insulation in buildings from laboratory measurements is becoming increasingly relevant for architects and designers. In North America, the apparent sound insulation is classified in terms of Apparent Sound Transmission Class (ASTC). The ASTC rating includes both the transmission through the separating assembly and the transmission via flanking paths. The National Research Council Canada has published a number of guideline documents that detail the calculation procedure for ASTC and provide the required laboratory data for various construction types. In NRC Research Report RR-335, “Apparent Sound Insulation in Cross-Laminated Timber Buildings” the focus is on buildings which are constructed from cross-laminated timber (CLT) panels. Measurements of the direct sound insulation of CLT panels and the vibration attenuation at their junctions were conducted at the NRC in recent years. The report RR-335 describes how to combine the relevant data to obtain estimates of the apparent transmission loss and the ASTC rating for a given CLT construction. This presentation will present highlights of the report and demonstrate the use of the Simplified Method and the Detailed Method to calculate the ASTC rating.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.230
Threshold uncertainty score0.178

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 head0.017
GPT teacher head0.261
Teacher spread0.244 · 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

Citations17
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueThe Journal of the Acoustical Society of AmericaSame topicWood Treatment and PropertiesFrench-language works237,207