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Designing for Spatial Sound in a Challenging Auditorium Restoration

2023· article· en· W4387869545 on OpenAlexaboutno aff
John O’Keefe

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsnot available
Fundersnot available
KeywordsCeiling (cloud)ReverberationArchitectural acousticsComputer sciencePoint (geometry)AcousticsSound (geography)Architectural engineeringEngineeringStructural engineeringPhysicsGeometryMathematics

Abstract

fetched live from OpenAlex

The York Memorial Auditorium in Toronto, Canada burnt to the ground on 7 May 2019. The original building, erected in 1929, was typical of the early 20th century architectural style for auditoria in Canada – wide and flat. The spatial experience of the sound in this room, and so many others like it, suffered accordingly. Two key elements in the restoration design will address this concern. The coffered ceiling will be opened up to significantly to increase the overall height of the room and, in so doing, improve both the Reverberation Time and the Early Decay Time/Reverberation Time ratio. Lateral reflections will also be improved with the inclusion of reflectors inside the newly available ceiling space. The reflectors have been designed with the aid of a novel Genetic Algorithm (GA) routine that has been developed by the author. Unlike previous routines, which optimise reflections from one point to another or from one point to a zone of points, the new GA optimises reflections from one zone of source points to another zone of receiver points.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0020.002
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.089
GPT teacher head0.330
Teacher spread0.242 · 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
Published2023
Admission routes1
Has abstractyes

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