Exterior Noise Ingress in Greater Vancouver Residential Buildings: Prediction versus Field Measurements
Bibliographic record
Abstract
Most municipalities in the lower mainland prescribe acoustic requirements for rezoning applications to study outdoor noise ingress and necessary facade improvements to meet interior noise limits such as those mentioned in the CMHC Road and Rail Noise: Effects on Housing criteria. However, there is no standard method of performing this assessment. As there are often no requirements for post-construction testing, the actual interior noise levels are not measured and compared with the initial predictions. After a number of assumptions and theoretical calculations, how accurate are these interior noise predictions? For a number of residential development projects, we conducted post-construction measurements to measure the actual noise level inside bedrooms. The measurement results were compared with the original predictions to assess the accuracy and identify tendencies to over- or under-predict. In addition, we looked at factors which could influence the accuracy such as facade noise exposure estimate, transmission loss data, and noise ingress calculation. This paper discusses our findings and recommended best practices for interior noise level predictions for Greater Vancouver residential construction.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".