Evaluating official marine protected area coverage for Aichi Target 11: appraising the data and methods that define our progress
Bibliographic record
Abstract
ABSTRACT The adoption of the Convention on Biological Diversity (CBD) Strategic Plan for Biodiversity, along with the 20 Aichi Targets, is a strong political endorsement for integrating biodiversity strategy across the entire United Nations system. Aichi Targets represent specific, time‐bound drivers for governments to safeguard both marine and terrestrial biodiversity. For the marine environment, Aichi Target 11 represents a call to effectively conserve at least 10% of coastal and marine areas by 2020. The core indicator to measure Aichi Target 11 is the extent of protected area coverage, and therefore it is essential that MPA data used to calculate this metric are robust. The World Database on Protected Areas (WDPA) is the authoritative source of data for measuring Aichi Target coverage progress. The WDPA assimilates global protected areas data as officially reported by the UN Member States themselves. Analysis of the WDPA (August 2014) calculated that MPAs now cover approximately 12,300,000 km2 or 3.41% of the world's ocean. Only 0.59% of the global ocean area (2 163 661 km2 within 1124 areas) is protected in no‐take areas. Only gathering and using State-sanctioned information may affect the accuracy of the WDPA MPA data. However, it is essential to first and foremost recognize national sovereignty and the rights of the Member State data providers in order to maintain a comprehensive approach to data gathering while ensuring international support for the resulting coverage figures that are used to measure global environmental targets. Further improvements could be made to the MPA data, for example by refining current MPA attributes and working with Member States and conventions to reduce or remove point data in the system. Moreover, broadening the scope of the WDPA to allow the inclusion of clearly marked non‐State‐sanctioned sites would complement existing official data and facilitate dialogue between Member States and other data providers towards MPA data improvement. Copyright © 2014 John Wiley & Sons, Ltd.
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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 | 0.245 | 0.455 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.018 | 0.028 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".