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Record W4400288015 · doi:10.1121/10.0026646

The cost of transparency: Balancing acoustic, financial, and sustainability considerations for glazed office partitions

2024· article· en· W4400288015 on OpenAlexaff
Caroline Harvey, Vincent Jurdic, Chris Pollock, W BONING

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2024
Typearticle
Languageen
FieldEngineering
TopicArchitecture and Computational Design
Canadian institutionsArup Group (Canada)
Fundersnot available
KeywordsTransparency (behavior)BusinessSustainabilityFinanceAccountingEnvironmental economicsComputer scienceEconomicsComputer security

Abstract

fetched live from OpenAlex

Substantial areas of modern offices may be glazed, including office partitions and doors. The desire for transparent connection between adjacent spaces may be driven by a need for natural lighting, the aesthetics of glass, and a desire for inclusivity and openness within organizations. At the same time, organizations require speech privacy for confidential communications, a requirement that relies on good sound isolation performance from glazing and seals. This paper examines the cost of transparency for modern offices, with a focus on balancing the acoustic performance of glazed partitions with spatial planning, post-pandemic occupancy patterns, financial costs, and the carbon cost of extensive glazing. Drawing on recent work to address poor sound isolation in a building with multiple small private offices with glazed partitions onto open office areas, this paper examines the impact of low Noise Isolation Class (NIC) values between adjacent spaces, including voice privacy concerns, acoustic discomfort and enforced changes to occupancy patterns. The design of glazed partitions should address a range of privacy needs while balancing the benefits and costs of a “transparent” workplace in terms of acoustics, construction costs, and embodied carbon.

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)0.000
metaresearch head score (Gemma)0.001
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: Methods · Consensus signal: none
Teacher disagreement score0.985
Threshold uncertainty score0.206

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.010
GPT teacher head0.243
Teacher spread0.233 · 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
GenreMethods

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
Published2024
Admission routes1
Has abstractyes

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