Reflections on How to Reach the “30 by 30” Target: Identification of and Suggestions on Global Priority Marine Areas for Protection
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it