Acoustical Improvements with Natural Air Ventilation in the Liu Institute for Global Issues at the University of British Columbia
Bibliographic record
Abstract
Abstract Low speech privacy in shared and private offices in one of the early generation of a “green” building resulted in occupants' dissatisfaction. This problem is experienced in Liu institute with a natural-ventilation system. Such a system requires low air-flow resistance which is achieved by large openings which will result in noise transmission between various spaces within the building. The poor acoustical quality in this building resulted in occupants' noise complaints which were further investigated by way of relevant acoustical measurements. CATT-Acoustic software was utilized to modify the acoustical quality of the building without any disturbance to the occupants. The optimized design of the transfer box above the office door was selected based on CATT-Acoustic predictions. The acoustical measurements were conducted after installation of the transfer box above the office door. The measurements' results agreed with the predictions which led to improved speech privacy to an acceptable level between the office and the corridor in Liu Institute. More work should be done to improve the acoustical quality of natural-ventilated building to conform to ANSI standards.1 The results of this study strongly support including acoustics in “green” building designs with natural ventilation to avoid users' complaints.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".