Bibliographic record
Abstract
The Ministry of Transportation (MoT) of the Province of B.C. noise abatement policy requires that environmental community noise impacts of highways projects involving new or substantially upgraded highways be assessed and mitigation implemented where warranted. This paper presents an extraordinary noise model developed to mitigate residential areas of the Village of Lions Bay on the rugged B.C. coastline from the Sea-to-Sky Highway connecting Vancouver, B.C. to Whistler, B.C. home of the Winter Olympic and Para-Olympic Games in 2010. The Cadna/A Version 3.9.15 software was used to develop a new 4 lane split grade alignment model incorporating two mini-interchanges. With the objective of achieving a 10 dBA noise reduction benefit, mitigation in the form of quiet pavement (OGA), traffic calming and 5 meter high absorptive sound walls or noise barriers were introduced into the model which included 150 mountain side residential receptors along the 2 km long corridor at elevations up to 35 meters above the project. Noise reduction benefits for dwellings were further analyzed to determine contributions during each stage of project development by creating a super user, modular, multi-layer noise model of Lions Bay. This work was carried out under the sponsorship of BCMoT.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.068 | 0.010 |
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".