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Record W4367145430 · doi:10.1121/10.0018948

Acoustic ceiling systems inside buildings: What we were taught and should now know instead

2023· article· en· W4367145430 on OpenAlexaboutno aff
Gary Madaras

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2023
Typearticle
Languageen
FieldHealth Professions
TopicNoise Effects and Management
Canadian institutionsnot available
Fundersnot available
KeywordsCeiling (cloud)Plenum spaceArchitectural engineeringComputer scienceModular designArchitectural acousticsAcousticsNoise controlMechanical engineeringEngineeringStructural engineeringNoise reductionArtificial intelligencePhysics

Abstract

fetched live from OpenAlex

This presentation is an executive summary of how modular, suspended, acoustic, ceiling systems perform inside buildings as presented at meetings of the Acoustical Society of America and noise control engineering conferences between 2015 and 2022. It bridges the gap between laboratory testing of ceiling panel metrics and how complete ceiling systems perform inside buildings when combined with other building elements such as lights, air diffusers, floor slabs, plenum barriers and mechanical devices in the plenum. Ceiling system performance will be discussed from the perspectives of complying with minimum sound absorption, minimum sound isolation and maximum background noise level requirements in building design standards and guidelines. Corroboration with foundational testing conducted by the National Research Council of Canada and ASHREA will be integrated. Standards with prediction methods that should be used instead of ceiling panel metrics will be reviewed. Citations to studies will be provided for more detailed information.

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)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.535
Threshold uncertainty score0.621

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.041
GPT teacher head0.356
Teacher spread0.315 · 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 designNot applicable
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
Published2023
Admission routes1
Has abstractyes

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