Evaluation of decision support tools for marine spatial planning in the Salish Sea
Bibliographic record
Abstract
The Marine Spatial Planning (MSP) initiative for the south coast of British Columbia (BC), Canada aims to bring together federal, provincial, and Indigenous partners, communities, and stakeholders to collectively coordinate how to use marine spaces and achieve ecological, economic, cultural, and social objectives. Decision support tools (DSTs) such as Marxan and Prioritizr are an important resource in the development of a marine spatial plan. These tools can systematically evaluate available spatial data on existing and future conditions to identify areas of high conservation value or importance for marine activities, define potential zoning frameworks, and evaluate trade-offs. A pilot study was initiated to evaluate DSTs and zoning approaches relevant to the development of a marine spatial plan for the BC South Coast, including the Salish Sea. This work builds off lessons learned in the use of DSTs to support the marine protected area network planning process in the North Coast of BC. DSTs that have been used to identify areas of high ecological or socioeconomic value and DSTs able to generate zoning scenarios were assessed, as were approaches for defining zones and incorporating conflicts and compatibilities between ecological features and marine activities. The results of this study can help partner organizations collaboratively identify major decision points, key supporting information, and workable spatial solutions throughout the process of developing a marine spatial plan for the BC South Coast.
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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 | 0.022 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".