Proof of concept: science is not a barrier to establishing networks of marine protected areas in the Scotian Shelf and Gulf of Maine
Bibliographic record
Abstract
No abstracts are to be cited without prior reference to the author.Well-planned networks of marine protected areas (MPAs) are recognized as a foundational step toward ecosystembased management and the precautionary principle, and Canada has committed to implementing such networks throughout its ocean waters by 2012. But effective network design necessitates a systematic approach – one which requires developing a comprehensive picture of the spatial distribution of ecological values, setting clear goals, and taking a transparent approach to designing networks that meet those goals. Although well-accepted in terrestrial systems, systematic conservation planning has only recently taken hold in the marine realm, and some argue that the science is not yet sufficiently developed to proceed. This talk will describe WWF-Canada’s methods and findings in carrying out a ‘proof of concept’ systematic MPA network plan for the Scotian Shelf and Gulf of Maine. The study examined whether the state of knowledge and experience about network design principles, conservation feature distribution data, and decision support tools is sufficient to begin planning and implementing MPA networks in Canada. The talk will conclude with a brief update on the ‘state of the art’ of systematic MPA network design and scientific guidance.
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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 | 0.043 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.070 | 0.020 |
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".