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Record W4312858971 · doi:10.1121/10.0016249

Virtual acoustic reconstruction of the Roman amphitheater of Avella in Italy

2022· article· en· W4312858971 on OpenAlexaff
Umberto Berardi, Gino Iannace, Amelia Trematerra, Antonella Bevilacqua

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2022
Typearticle
Languageen
FieldHealth Professions
TopicNoise Effects and Management
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsArchitectural acousticsAcousticsPlan (archaeology)Computer scienceSound (geography)ArchitectureVirtual modelGeologyComputer graphics (images)ArtVisual artsPhysicsPaleontology

Abstract

fetched live from OpenAlex

In ancient Rome, gladiator fights were very popular. The places where these shows took place were called amphitheaters, due to the particular shape of the elliptical building. Amphitheaters were widespread, and in each city, one or more of these buildings was present. This paper describes the virtual reconstruction of the acoustics of the amphitheater Avella, close to Naples, Italy. This amphitheater was discovered a few decades ago and was only partially rebuilt. Today, it is used for musical performances during the summer season mainly. The plan of the building is elliptical, and the dimensions of the arena are 35 m for the minor axis and 65 m for the major axis. Acoustic measurements were performed in the current state with an impulsive sound source and the acoustic parameters were obtained according to the ISO 3382 standard. Subsequently, from the dimensions of the current state and in analogy with the architecture of other amphitheaters existing today, a virtual reconstruction of the original shape was performed. The virtual model was developed with the architectural acoustics software Ramsete in order to obtain the acoustic characteristics of the amphitheater as it was in Roman times. In particular, the spatial distribution of the acoustic characteristics on the steps where the audience was seated is described and discussed.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.412
Threshold uncertainty score0.403

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.016
GPT teacher head0.303
Teacher spread0.287 · 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 designSimulation or modeling
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
Published2022
Admission routes1
Has abstractyes

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