MétaCan
Menu
Back to cohort
Record W2300896642 · doi:10.3390/buildings6010011

Acoustics of a Music Venue/Bar—A Case Study

2016· article· en· W2300896642 on OpenAlexaffabout
Ramani Ramakrishnan, Romain Dumoulin

Bibliographic record

VenueBuildings · 2016
Typearticle
Languageen
FieldHealth Professions
TopicNoise Effects and Management
Canadian institutionsToronto Metropolitan University
FundersForskningsrådet om Hälsa, Arbetsliv och Välfärd
KeywordsBar (unit)PianoDoorsSound (geography)JazzAcousticsUnit (ring theory)Visual artsEngineeringArchitectural engineeringComputer scienceArtGeographyMechanical engineeringMathematicsPhysicsMeteorology

Abstract

fetched live from OpenAlex

A vacant unit, once used by a Portuguese Deli, was converted to a bar/music room in Toronto. The unit was divided into two spaces along its north-south axis. The western portion was designed as a music room that would provide a performance space from a solo artist to a Jazz combo to a small rock band. The eastern part was designed as a regular bar/dining area. The plan also called for a microbrewery unit at the back of the unit. The bar music can be loud, while the music room can be pianissimo to forte depending on the type of performance. The acoustical design aspects are critical for the music room. In addition, the acoustical separation between the two spaces is equally important. The music room/bar is currently in use. The design results are compared to actual field measurements. The results showed that the music venue performed satisfactorily. The acoustical separation between the music venue and the bar/restaurant was better than expected other than an installation deficiency of the south side sound lock doors. The background sound along the northern portion was NC-35 or less. However, the southern portion’s background sound exceeded NC-35 due to the hissing of the return air grille. The acoustical design and the performance results of the music venue-bar/restaurant are presented in this paper.

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)0.001
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.262
Threshold uncertainty score0.313

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.060
GPT teacher head0.402
Teacher spread0.342 · 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

Citations5
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueBuildingsSame topicNoise Effects and ManagementFrench-language works237,207