IdentIfyIng prIorIty areas for marIne conservatIon In BrItIsH coLUmBIa: a coLLaBoratIve approacH
Bibliographic record
Abstract
Implementation of marine conservation measures has been slow in British Columbia, providing the motivation for initiating the British Columbia Marine Conservation Analysis (BCMCA) project. The purpose of the BCMCA project is to collaboratively identify areas of high conservation interest for the marine waters of British Columbia. The project team is comprised of representatives from academia, First Nations organizations, non-profit environmental groups, federal and provincial government agencies, and user groups. The BCMCA project is developing two products: 1) an atlas of known ecological and human use values; and 2) a series of Marxan spatial analyses. The atlas will map ecological data, human use data, areas where data are lacking, and a combination of areas of ecological value and more intensive human use. The Marxan spatial analyses will iteratively identify: 1) areas with high conservation value based on ecological data only; 2) areas of high conservation value that minimize overlap with areas important to human use; and 3) areas of high conservation value that incorporate additional marine reserve design considerations. To guide and inform the analysis, we held five ecological expert workshops focused on various ecosystem components, are engaging user groups, and have a workshop planned to refine analysis methods. The ecological workshops drew on the knowledge and expertise of resource managers, the conservation community, academics and First Nations, to help assemble and use the best available data – biological, ecological and oceanographic – in developing sound, defensible analysis methods and products. Results of the BCMCA project are intended to advance marine planning initiatives in British Columbia by collaboratively and iteratively identifying potential areas of high conservation value.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.019 | 0.024 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".