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Record W4400286168 · doi:10.1121/10.0026755

Sound transmission loss of vertical building partitions designed to meet shear structural requirements

2024· article· en· W4400286168 on OpenAlexaboutno aff
Benjamin M. Shafer

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2024
Typearticle
Languageen
FieldEngineering
TopicStructural Engineering and Vibration Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsSound transmission classSound (geography)Shear (geology)Transmission (telecommunications)Computer scienceAcousticsStructural engineeringEngineeringGeologyTelecommunicationsCivil engineeringPhysics

Abstract

fetched live from OpenAlex

Vertical partitions often include structural building elements designed to meet shear requirements. Shear partitions are designed to resist lateral load due to wind or seismic activity and are required in many regions throughout North America. Nightingale et al. completed a 70-assembly series quantifying the effect of shear OSB on the sound transmission loss (STL) of both wood- and steel-framed single-stud assemblies in the National Research Council Canada (NRCC) research report NRCC-45018. This research study builds on the NRCC research with a test of over 200 wood and steel assembly partitions. This research study includes single-, staggered-, and double-stud framing as well as various forms of shear treatment such as OSB Plywood installed on one or both sides of the partition, attachment with various nail attachment patterns, and steel strapping. The effect of these partition elements on the STL will be presented and discussed. Additionally, the effect of sound isolation treatment, such as additional mass, resilient channels, resilient sound isolation clips, and constrained-layer damping will be presented and discussed.

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: none
Teacher disagreement score0.840
Threshold uncertainty score0.232

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 head0.014
GPT teacher head0.272
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 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
Published2024
Admission routes1
Has abstractyes

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