The MPA Guide: A framework to achieve global goals for the ocean
Bibliographic record
Abstract
Marine Protected Areas (MPAs) are conservation tools intended to protect biodiversity, promote healthy and resilient marine ecosystems, and provide societal benefits. Despite codification of MPAs in international agreements, MPA effectiveness is currently undermined by confusion about the many MPA types and consequent wildly differing outcomes. We present a clarifying science-driven framework—The MPA Guide—to aid design and evaluation. The guide categorizes MPAs by stage of establishment and level of protection, specifies the resulting direct and indirect outcomes for biodiversity and human well-being, and describes the key conditions necessary for positive outcomes. Use of this MPA Guide by scientists, managers, policy-makers, and communities can improve effective design, implementation, assessment, and tracking of existing and future MPAs to achieve conservation goals by using scientifically grounded practices.
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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 | 0.028 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.012 | 0.009 |
| Insufficient payload (model declined to judge) | 0.016 | 0.010 |
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".