Incentivizing More Effective Marine Protected Areas with the Global Ocean Refuge System (GLORES)
Bibliographic record
Abstract
Healthy oceans are essential to human survival and prosperity, yet oceans are severely impacted worldwide by anthropogenic threats including overfishing, climate change, industrialization, pollution, and habitat destruction. Marine protected areas (MPAs) have been implemented around the world and are effective conservation tools that can mitigate some of these threats and build resilience when designed and managed well. However, despite a rich scientific literature on MPA effectiveness, science is not the main driver behind the design and implementation of many MPAs, leading to variable MPA effectiveness and bias in global MPA representativity. As a result, the marine conservation community focuses on promoting the creation of more MPAs as well as more effective ones, however no structure to improve or accelerate effective MPA implementation currently exists. To safeguard marine ecosystems on a global scale and better monitor progress towards ecosystem protection, robust science-based criteria are needed for evaluating MPAs and synthesizing the extensive and interdisciplinary science on MPA effectiveness. This paper presents a strategic initiative led by the Marine Conservation Institute called the Global Ocean Refuge System (GLORES). GLORES aims to set standards to improve the quality of MPAs and catalyze strong protection for at least 30% of the ocean by 2030. Such substantial increase in marine protection is needed to maintain the resilience of marine ecosystems and restore their benefits to people. GLORES provides a comprehensive strategy that employs the rich body of MPA science to scale up existing marine conservation efforts.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".