Geographic Response Strategies on Canada's West Coast
Bibliographic record
Abstract
Abstract In 2012, Western Canada Marine Response Corporation (WCMRC) began developing site specific shoreline protection strategies, known at Geographic Response Strategies (GRS) for the entire coast of British Columbia (B.C.). The project started in Vancouver Harbour and has since spread along the Salish Sea and Strait of Juan de Fuca, as well as into Prince Rupert and Kitimat on the northern B.C. coast. Recognizing that B.C. has approximately 27,000 km of coastline (~16,777 miles) and with 450 strategies already developed only within a few hundred kilometres, WCMRC saw a need to automate the GRS development process from data collection all the way to the final GRS output. In conjunction with a local environmental consulting company, WCMRC developed a new sensitivity model. This new model can help the Response Readiness Team quickly assess intertidal sensitivity to oiling based on shoreline type, oil residency index, biological, archaeological, and/or socio-economic features of the shoreline, as well as operational protection feasibility. Now, using ESRI GIS web tools, a GRS can be developed automatically as a geo-referenced PDF, easily exportable to mobile devices for operational use. Overall, the automated enhancements have provided WCMRC with the tools necessary to manage the GRS program for B.C.'s entire coast. This means that more coastline can be assessed far more quickly and GRS's can be developed using fewer human resources. Additionally, if a spill occurs in a more remote area that has not yet had GRS's developed, they can be created within minutes based on the information from the Environment Unit in the Incident Command Post, or initial assessments by responders.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".