Quantifying the ambient community noise environment for optimal industry siting
Bibliographic record
Abstract
Urban noise is an emerging nuisance issue for growing communities. The analysis method discussed herein can be used to industry's advantage. A road traffic noise model was developed by Conestoga-Rovers & Associates (CRA) to approximate the ambient community noise levels present within a 200 km2 project area. Road corridors that included highways, city streets, and country side-roads were modeled to evaluate the existing road traffic generated ambient noise environment. An acoustical model and US Department of Transportation Federal Highway Administration Traffic Noise Model calculation standard was used to account for a variety of real-world variables such as Daily Average Traffic Counts, turning counts, speed limits, road composition, elevation, road width, and traffic composition. The model generated noise contours that were used to identify areas of elevated ambient noise levels within the project area that may prove suitable for a medium-sized industrial facility. This quantification of the ambient community noise environment allowed for the identification of optimal industrial sites within the project area. Locating new facilities within urbanized areas with elevated ambient conditions promotes complementary adjacent land use and sustainable urban densification by minimizing adverse community noise impacts and reducing post-construction noise abatement costs for industry.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 0.000 |
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".