Commissioning the Acoustical Performance of an Open Office Space Following the Latest Healthy Building Standard: A Case Study
Bibliographic record
Abstract
Healthy building design guides are cogent and necessary. While elements that contribute to healthy buildings are multifactorial, the perception of sound versus noise is subjective and difficult to operationalize. To inform the commissioning process, the acoustics in an open office was examined following the first international building certification system that focuses on the well-being of occupants. Results highlight the role facility managers play in ensuring acoustical quality and offer suggestions to optimize healthy building rating systems. Mixed empirical evidence concerning the advantages of open office designs exists, as does evidence that noise, and a lack of privacy, affects workers’ levels of distraction and dissatisfaction. Sound masking systems can lower stress levels and augment performance. However, the sound produced by these systems can also be disruptive; conflicting information exists for facility managers to use when making decisions. The results suggest that, although objective measurements and healthy building guidelines for designing satisfactory indoor acoustic environments are important, changes to the physical environment, and acoustical systems, in particular, require iterative subjective assessments within the retrofit process to bolster occupant satisfaction. Mixed-methodologies used in this study may aid facilities managers in capturing and interpreting occupant data about physical stimuli in the workplace and improving the commissioning process.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".