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Record W2061623818 · doi:10.1121/1.4743797

Differences in performance with different background sound and ambient noise in three open office plans

2000· article· en· W2061623818 on OpenAlexaboutno aff
Heakyung Yoon

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2000
Typearticle
Languageen
FieldPsychology
TopicFacilities and Workplace Management
Canadian institutionsnot available
Fundersnot available
KeywordsOpen planNoise (video)AcousticsSound (geography)Computer scienceQUIETTest (biology)Binaural recordingSimulationApplied psychologyPsychologyEngineeringArtificial intelligenceSpeech recognitionPhysics

Abstract

fetched live from OpenAlex

The effect of noise on the performance of workers is investigated in three types of open workplaces: a combination of open plan and closed offices, rectangular configuration in open plan, and triangular configuration in open plan. An open workplace is intended to increase workers’ performance by creating a knowledge-sharing environment informally and seamlessly, while it is also known to decrease workers’ performance for they are annoyed with or distracted by the sound from the workplace. According to interviews from and questionnaires completed by workers in these workplaces, the order of preference was the combination, the rectangular configuration, and the triangular configuration workplaces. Each subject took two proofreading tests for 10 min under two different sound conditions. One of these conditions was background sound only with quiet HVAC sound and the other was ambient sound, which was recorded from each workplace along with speech and the sound from other activities by a binaural artificial head measurement system. The result is the mean test score of subjects from the triangular configuration workplace is significantly different from the mean test scores of subjects from the combination type workplace and the rectangular configuration workplace, respectively. [Work supported by IBM-Toronto, Inc.]

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.213
Threshold uncertainty score0.629

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.025
GPT teacher head0.263
Teacher spread0.239 · 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 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

Citations2
Published2000
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of the Acoustical Society of AmericaSame topicFacilities and Workplace ManagementFrench-language works237,207