Marine Protected Areas and Other Effective Area-based Conservation Measures
Bibliographic record
Abstract
As the Earth’s changing climate has deepened into a climate crisis, the Arctic region has emerged as one of the clearest indicators of the scale and pace of that change. As the ice melts, opportunities are expanding to exploit the Arctic’s oil and gas reserves, precious metals, fish stocks and maritime routes. Increased access and development will inevitably generate “system-wide environmental impacts” and will pose novel management challenges for the Arctic states. In the quest to find an effective balance between competing ocean activities and actors, marine protected areas (MPAs) and other effective area-based conservation measures (OECMs) have emerged as indispensable tools to achieve ocean health, including in the Arctic. After first introducing these concepts, this article will discuss the Canadian and Russian domestic regimes for the establishment of MPAs and OECMs. The conclusion will then offer some insights into the key challenges confronting both states in the creation of effective networks of MPAs and OECMs in their Arctic regions.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".