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Record W7062486507

The Transmission Loss of Double Stud Walls with Layers of Gypsum Board Installed inside the Wall Cavity

2024· article· en· W7062486507 on OpenAlexvenueno aff

Bibliographic record

VenueNPARC · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdaptive optics and wavefront sensing
Canadian institutionsnot available
Fundersnot available
KeywordsGypsumOctave (electronics)Cavity wallTransmission lossOn boardOriented strand boardTransmission (telecommunications)
DOInot available

Abstract

fetched live from OpenAlex

A demising wall assembly that is frequently being specified for mid-rise and high-rise building constructions is a double steel stud wall assembly with one or more sheets of fire rated gypsum board installed inside the wall cavity between the rows of studs, creating a triple leaf wall. Locating the gypsum board inside the wall cavity can sharply decrease the transmission loss below the 200 Hz one-third octave band due to the creation of two mass-air-mass resonances centered around the 80 Hz one-third octave band. For the walls tested as part of this study, this decrease in the transmission loss was 14 to 17 dB. One theory for why gypsum board is being specified inside the wall cavity is the belief that the gypsum board in the wall cavity maintains the transmission loss of the wall even if residents create small holes in the outer gypsum board layers by hanging decorations on the wall or securing furniture to the wall. To disprove this theory, an increasingly larger number of holes were drilled into the gypsum board on both sides of a wall to determine the effect on the transmission loss. It was found that a significant number of holes, well in excess of normal use of a wall needed to be drilled into the gypsum board before the transmission loss was affected.

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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.241
Teacher spread0.228 · 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
Published2024
Admission routes1
Has abstractyes

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