Reflections on How to Reach the “30 by 30” Target: Identification of and Suggestions on Global Priority Marine Areas for Protection
Bibliographic record
Abstract
The establishment of marine protected areas (MPAs) is an important method to ensure marine protection. To protect and conserve global marine biodiversity, with the adoption of the “Kunming-Montreal Global Biodiversity Framework” during the 15th meeting of the Conference of the Parties of Convention on Biodiversity (CBD) in December 2022, the establishment of an effectively managed MPA network by 2030 and the protection of 30% of the world’s oceans will be common goals for all countries party to the CBD over the next decade. Based on the distribution of over 150 types of marine species, habitats, ecosystems, and abiotic elements, ArcGIS10.5 and Zonation are used in this study to calculate the marine protection priority levels of coastal, nearshore, open ocean, and deep ocean trench areas, and a plan to reach the “30 by 30” targets is proposed. The suggestions for scientifically identifying and managing MPAs are as follows: first, improve MPA planning and establish a well-connected MPA network in national jurisdictions, then conduct scientific marine investigations to obtain background data on MPA establishment and delimitation.
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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 | 0.021 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.012 | 0.020 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".