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Sustainable Sonic Environments - The Robotic Fabrication of Mass Timber Acoustic Surfaces

2020· article· en· W4286419848 on OpenAlexaff
Brady Peters, Nicholas Hoban, Krystal Kramer

Bibliographic record

VenueProceedings of the International Conference on Computer-Aided Architectural Design Research in Asia · 2020
Typearticle
Languageen
FieldEngineering
TopicArchitecture and Computational Design
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsConstructiveArchitectureArchitectural engineeringComputer scienceSound (geography)FabricationAcousticsEngineeringProcess (computing)Physics

Abstract

fetched live from OpenAlex

This research proposes that mass timber panels can not only enable a new type of architecture that is sustainable, but that also sounds better. As mass timber construction often exposes the wood structure, and these panels are carefully constructed in factory settings, these panels have the potential to be built so that the acoustically absorptive, reflective, or sound scattering acoustic properties of surfaces can be integrated into the constructive logic and architectural aesthetic of the building. This paper specifically investigates the potentials of the sound scattering performance of cross laminated timber (CLT) panels. Through design, simulation, and prototyping various surface designs are investigated.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.436
Threshold uncertainty score0.532

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.000
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 head0.073
GPT teacher head0.287
Teacher spread0.214 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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