Session Seven: Protecting Special Places MARINE PROTECTED AREAS (MPAS) IN REGIONAL CONSERVATION PLANNING: A FRAMEWORK FOR THE SCOTIAN SHELF AND GULF OF MAINE
Bibliographic record
Abstract
Marine protected areas (MPAs) offer a range of benefits for fisheries, local economies and the marine environment. They serve to prevent habitat damage, maintain biodiversity, and provide a safe haven for fish stocks to recover (Halpern 2003; Gell and Roberts 2003; Roberts et al. 2001; Ward et al. 2001 and others). MPAs, therefore, are an insurance policy for the future, both for marine life and local people. The scientific consensus is growing: setting aside some areas that are managed for conservation is critical to achieving healthy oceans and oceans-based economies. Despite this, spatial conservation tools are currently underused in marine ecosystems; less than one percent of the world’s oceans has any meaningful protection. There is still a great distance to go if we are to implement the protection needed to restore the habitats and living resources of our oceans. For these reasons, WWF-Canada is working towards establishing networks of MPAs. Networks of Protected Areas Unless they are very large, individual MPAs are unlikely to capture the full range of habitats characteristic of a large marine ecosystem. They are also unlikely to contain the full range of life history stages for migratory species or species that spend part of their life history floating freely as plankton. Although implementing individual MPAs is important, they can be made more effective and their im-
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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 | 0.015 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.018 | 0.011 |
| Insufficient payload (model declined to judge) | 0.064 | 0.021 |
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".