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Record W2171610532 · doi:10.13140/2.1.2298.8800

SUPPORTING BETTER NOISE CONTROL IN CANADIAN BUILDINGS

2014· article· en· W2171610532 on OpenAlexvenueaboutno aff
Ivan Sabourin, Berndt Zeitler, David Quirt

Bibliographic record

VenueCanadian acoustics · 2014
Typearticle
Languageen
FieldHealth Professions
TopicNoise Effects and Management
Canadian institutionsnot available
Fundersnot available
KeywordsCeiling (cloud)Noise controlKey (lock)Transmission (telecommunications)Control (management)EngineeringNoise (video)Sound transmission classFlanking maneuverSet (abstract data type)Architectural engineeringClass (philosophy)Computer scienceSystems engineeringTelecommunicationsTransport engineeringCivil engineeringComputer securityStructural engineeringNoise reduction

Abstract

fetched live from OpenAlex

A simplistic requirement for minimum STC of the wall or floor/ceiling assembly separating adjacent units in multi-family residential buildings has been used in North American building codes for over 50 years. Effective noise control requires replacing the traditional design objective with a requirement including the effect of flanking transmission, such as the Apparent Sound Transmission Class (ASTC). Such a transition requires a supporting set of technical standards for measuring direct and flanking sound transmission for typical assemblies and junctions, plus a credible procedure for calculating system performance from these inputs. Implementing a new approach in practice also needs technical support including calculation tools suitable for both the design and regulatory communities, and data on performance of typical generic constructions. This paper discusses key projects at the National Research Council of Canada both to establish the technical infrastructure supporting such a change in Canada’s building codes and to provide the tools needed by designers seeking to provide enhanced noise control.

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)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.072
Threshold uncertainty score0.520

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0060.002
Scholarly communication0.0030.001
Open science0.0020.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.012
GPT teacher head0.329
Teacher spread0.318 · 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 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

Citations2
Published2014
Admission routes2
Has abstractyes

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