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Record W2105871722

STC Ratings of Drywall Partitions with and without Structural Sheathing

2015· article· en· W2105871722 on OpenAlexvenueno aff
Alex Lorimer, Jessica Tinianov

Bibliographic record

VenueCanadian acoustics · 2015
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Phenomena Research
Canadian institutionsnot available
Fundersnot available
KeywordsStructural engineeringSound transmission classPartition (number theory)EngineeringDimension (graph theory)Code (set theory)Frame (networking)Transmission (telecommunications)Computer scienceAcousticsCivil engineeringMathematicsTelecommunicationsPhysics
DOInot available

Abstract

fetched live from OpenAlex

It is often encountered that architects or other building designers specify multiple cavity partitions, with the expectation that they provide equal or greater sound transmission loss performance than a single cavity partition of similar overall mass and dimension. Previous studies confirm that this is not generally true. In particular, the National Building Code lists wood-frame and drywall demising partitions with single large cavities and relatively high Sound Transmission Class (STC) ratings; however, a footnote indicates that the structural sheathing configurations in these walls can degrade the performance from slightly to unpredictably. This article presents a comparison of several field measurements demonstrating the significant acoustical effects of a commonly specified sheathing configuration.

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)0.001
metaresearch head score (Gemma)0.007
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 score0.010
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0100.002

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 head0.021
GPT teacher head0.231
Teacher spread0.210 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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