Developing The Port of Vancouver’s Port Noise Rating Methodology
Bibliographic record
Abstract
The Vancouver Fraser Port Authority manages Canada’s largest port, the Port of Vancouver, encompassing federal port waters, lands, and shorelines in and around Vancouver, BC. In total, port-managed areas border 16 municipalities. As urban densification has increased near port operations, so too has the potential for port-related noise to disturb nearby communities. Between 2013 and 2015, to better understand port-related noise in nearby communities and address community noise concerns, the port installed 11 permanent noise monitoring terminals (NMTs) along the north and south shores of Burrard Inlet and at Roberts Bank. The NMTs continually log sound data in or near communities potentially affected by port noise. In order to provide value from this data to the port and its stakeholders, BKL developed a Port Noise Rating (PNR) metric which relates the measured sound pressure levels to the potential annoyance in the surrounding population using noise modelling and census data. The PNR metric provides a useful way for the port to interpret the significance of measured noise levels, changes over time, and differences between NMTs. However, the accuracy of this approach depends on many noise source modelling assumptions due to the complex noise environments that exist throughout the port’s jurisdiction. This paper summarizes the methodology BKL developed in partnership with the Vancouver Fraser Port Authority to calculate and present the PNR, including NMT configuration, local housing and population review, noise source identification, data analysis, and noise modelling.
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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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".