Case study: Computer modeling the noise produced by a future food court radiated to nearby existing offices
Bibliographic record
Abstract
Lately, there’s an increase in the co-existence of spaces of different usage with acoustical challenge. Throughout 2016 to 2018, the 2nd floor of the Complexe Les Ailes, in Montreal, underwent a fit-out to become a food court. At its center, there is an elliptical atrium where all levels are can be viewed. There are existing offices above the food court separated by a glass pane. The purpose of this study was to evaluate the noise generated by human activities in the future food court to the adjacent offices and recommend noise control elements if needed. Multiple sound samples were taken at existing food courts to quantify the sound level. Simulations were done with ODEON, a room acoustic software, to evaluate the sound level produced by different activities. On-site measurements were conducted to calibrate the existing conditions with the 3D ODEON model. Finally, noise reduction tests were undertaken to determine the sound levels that would be radiated in the offices by the activities in the food court. To reduce noise disturbance in the offices, acoustical treatment under the ceiling of the food court was recommended and the 3rd and 4th floor glass panes composition would have to be improved.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| 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".