Salish Sea Initiative Interactive Map
Bibliographic record
Abstract
The Salish Sea Initiative (SSI) is a Government of Canada, Trans Mountain Expansion (TMX) Accommodation Measure designed to respond to First Nations concerns about the potential environmental impacts of human activities on coastal and marine ecosystems in the Salish Sea. Led by Fisheries and Oceans Canada, the SSI aims to support the capacity building of eligible First Nations within and around the Salish Sea to plan, develop and conduct marine stewardship activities, including environmental monitoring, traditional use studies, and cumulative effects assessments. Thirty-three First Nations are eligible to participate in SSI and the initiative runs until March 2024. A key component of the SSI is the co-development of the SSI Interactive Map (SSIM). The SSIM is a user-friendly, decision support tool that displays data layers of natural marine environmental components, stressors and Indigenous cultural components. The purpose of the map is to provide a platform for SSI participants to visualize valued components (VCs) and other information that will be useful for project planning, implementation of marine stewardship work and cumulative effects assessments. The map is associated with a data catalogue and portal and functions are being created to enable data analysis. The map also serves as a communication tool to host conversations between SSI participants and between SSI participants and the Government of Canada. Enhanced communication capabilities provide support for project planning and coordination, the creation of partnerships, as well as a shared platform for inter-generational knowledge transfer opportunities within communities. The purpose of this presentation is to outline the background and process associated with the SSIM creation and to provide a demonstration to show the work completed to date. We will also highlight future actions to be taken for map enhancement.
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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 | 0.003 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.192 | 0.045 |
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".