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Conversion of a church into a concert hall: discussions about concerns and acoustic design solutions

2023· article· en· W4387870806 on OpenAlexaff
Silvana Sukaj, Luigi Guerriero, Gino Iannace, Giovanni Amadasi, Umberto Berardi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldArts and Humanities
TopicDiverse Musicological Studies
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsAcousticsComputer scienceEngineeringPhysics

Abstract

fetched live from OpenAlex

Italy is a country rich in historical monuments such as: churches, theatres, squares, historic buildings, castles. The problem is to use these buildings for the purposes of possible cultural enjoyment. Thus it happens that churches can change their original intended use and be used as cultural attractors to host conferences or concerts. One possible solution is to change the acoustics of these places to adapt to concert or conference halls. The St Spirit’s church in Aversa, located north of Naples in Italy, is one of the historical-artistic buildings to be valorized. Built in the 18th century, the church was destroyed by the 1980 earthquake. Restoration work began in 2021 with the reconstruction of a new roof and the restoration of the floor and walls. This contribution deals with the acoustic project of the church. Inside the church, the construction of a stage in place of the altar and the installation of sound-absorbing panels are planned. To develop the project, acoustic measurements were carried out and, with the aid of dedicated software for architectural acoustics, possible solutions to improve the room’s acoustics were studied.

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)0.013
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0060.007
Scholarly communication0.0120.009
Open science0.0030.004
Research integrity0.0070.007
Insufficient payload (model declined to judge)0.0080.001

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 head0.230
GPT teacher head0.284
Teacher spread0.054 · 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 designQualitative
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
Published2023
Admission routes1
Has abstractyes

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