Noise Levels from Heavily Travelled Roads for Use with Environmental Noise Regulations in British Columbia and Alberta
Bibliographic record
Abstract
Noise Impact Assessments (NIAs) for oil and gas facilities in British Columbia are typically conducted in accordance with the Noise Control Best Practices Guideline of the British Columbia Oil & Gas Commission (BC OGC). NIAs for industrial facilities in Alberta are typically conducted in accordance with Alberta Utilities Commission (AUC) Rule 012 or Alberta Energy Regulator (AER) Directive 038. The BC OGC Guideline, AUC Rule 012, and AER Directive 038 are very similar with respect to assessment methodology and compliance limits. All three documents require that noise be assessed cumulatively and, in particular, require that the contribution of heavily travelled roads be included when testing noise compliance for industrial facilities. All three documents endorse the same desktop technique for estimating A-weighted Ambient Sound Levels (ASLs) at various distances from heavily travelled roads. This paper compares ASL values estimated using the regulatory desktop technique and noise levels calculated using widely-accepted computer models of road traffic noise. Noise levels are compared for various receptor distances and various traffic levels. This paper uses modelled results to propose new ASL values for use in NIAs when traffic levels and/or receptor distances are not adequately addressed by the regulatory desktop technique.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".