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Record W4400288024 · doi:10.1121/10.0027362

Impact sound insulation performance of mass timber floors

2024· article· en· W4400288024 on OpenAlexaff
Jianhui Zhou, M.H. Asadijafari

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2024
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Phenomena Research
Canadian institutionsUniversity of Northern British Columbia
Fundersnot available
KeywordsSound (geography)SoundproofingArchitectural engineeringForensic engineeringEnvironmental scienceEngineeringComputer scienceAcousticsPhysics

Abstract

fetched live from OpenAlex

Mass timber floors are being used increasingly in both mass timber and hybrid timber buildings due to its dry and rapid construction. Bare mass timber structural slabs have relatively low impact sound insulation performance. Though certain floating floor assemblies on mass timber slabs can provide adequate single number ratings, such assemblies are mainly effective in the middle to high frequency range. This paper will provide an overview of the research on the impact sound insulation perforamnce of mass timber floors with exposed ceiling and floating floor assembies conducted at the Universtiy of Northern British Columbia. The test data of bare mass timber floors, with continous floating concrete toppings, with raised discrete floating floor assemblies will be presented and compared. The effect of interlayer, concrete thickness and excitation sources on the single number ratings and frequency spectra will be discussed. Recommendations for future reseach will be given based on the discussion.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.013
GPT teacher head0.270
Teacher spread0.257 · 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 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

Citations1
Published2024
Admission routes1
Has abstractyes

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