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Record W2038684342 · doi:10.1121/1.4806453

Planned versus achieved acoustical performance and user satisfaction for an office fit-out

2013· article· en· W2038684342 on OpenAlexaff
Ryan Bessey

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2013
Typearticle
Languageen
FieldPsychology
TopicFacilities and Workplace Management
Canadian institutionsGolder Associates (Canada)
Fundersnot available
KeywordsOpen planComputer scienceArchitectural engineeringIntelligibility (philosophy)Plan (archaeology)AcousticsEngineeringCivil engineering

Abstract

fetched live from OpenAlex

The purpose of the project was to move 500 office workers separated between two aging buildings into one new larger building. To this end, a team was formed to create an office fit-out design including several open plan offices, private offices, meeting rooms, boardrooms, a conference center, and a cafeteria to connect with the base-building infrastructure. Our role, as part of the design team, was to provide guidance such that the acoustical performance for new spaces was at least equivalent but preferably better than their existing counterparts. Factors that were considered included acoustic comfort, background sound level, speech privacy, and speech intelligibility. This paper summarizes our approach to the project, some difficulties that were encountered during construction, measured acoustical performance in both the newly constructed and existing buildings, and the statistical change in user satisfaction before and after the move determined via surveys.

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)0.003
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.028
GPT teacher head0.289
Teacher spread0.260 · 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 designObservational
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
Published2013
Admission routes1
Has abstractyes

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