MétaCan
Menu
Back to cohort
Record W2947394504 · doi:10.1121/2.0001005

Exploring acoustical approaches for pre-screening the airtightness of building enclosures

2018· article· en· W2947394504 on OpenAlexaff
Umberto Berardi, Sanam Pouyan

Bibliographic record

VenueProceedings of meetings on acoustics · 2018
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Phenomena Research
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsEnclosureSoundproofingBuilding envelopeSound transmission classInfiltration (HVAC)EngineeringAcousticsComputer scienceTransmission lossStructural engineeringEnvironmental scienceCivil engineeringTelecommunicationsMeteorology

Abstract

fetched live from OpenAlex

The air infiltration through the building enclosure plays a significant role in the energy performance of a building and in the ability to obtain comfortable conditions. The building airtightness is typically detected using experimental tools such as blower door devices, together with other approaches such as tracer gas methods. However, the estimation of the building airtightness through these methods can be expensive, time-consuming, and dependent on weather conditions. The importance of a rapid estimation of the air infiltration through a building envelope suggests to search for easier assessing methods. In this paper, the acoustic method as proposed in the ASTM E1186 is considered, and the correlations between the sound transmission loss of several windows and their airtightness levels are explored. The results reveal a poor correlation between the airtightness of the windows and their sound insulation performance. However, the weak relationship discourages the use of simple acoustic approaches based on the simple sound transmission loss assessment and reinforces the need to adopt beamforming techniques for accurate assessments of building enclosure deficiencies.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.616
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 integrity0.0000.000
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 head0.125
GPT teacher head0.289
Teacher spread0.164 · 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 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

Citations3
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of meetings on acousticsSame topicAcoustic Wave Phenomena ResearchFrench-language works237,207