3D NOISE MODELING AND ASSESSMENT IN THE DESIGN OF RESIDENTIAL AREAS
Bibliographic record
Abstract
Three-dimensional maps showing acoustic exposure can be used as information basis for residential areas planning and design in cities. This research work focuses on estimating changes in acoustic pollution in a large city caused by a new residential quarter being built there; overall, the quarter will be made up of 28 buildings including a school and a trade and entertaining center. The highest buildings in the quarter should not exceed 25 floors. Calculated estimates were performed at various heights, starting from 1.5 meters and up to 75 meters above the ground. Calculation results obtained for various heights allowed building up a threedimensional exposure picture for assessing expected levels of external noise at each floor in an apartment block. All calculations were made and acoustic exposure was visualized with a geoinformation system (ArcGIS 9.3 with ArcScene module) that allowed showing geographic and attributive data concerning an examined territory. Our experience and methodical approaches to estimating and visualizing noise propagation will allow making well-grounded managerial decisions on city development. Noise factor assessment is a key element in creating a favorable living environment in a city.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".