Mexico on Track to Protect 30% of Its Marine Area by 2030
Bibliographic record
Abstract
Mexico has committed to protecting 30% of its marine territory by 2030 to comply with Target 3 of the Kunming–Montreal Global Biodiversity Framework, adopted during the 15th Conference of the Parties to the Convention on Biological Diversity. In this paper, we demonstrate the feasibility of meeting this commitment by determining the marine extent of conservation measures based on legally established Marine Protected Areas and areas that meet the criteria to be considered as Other Effective area-based Conservation Measures (OECMs) and determining the marine extent of areas proposed in various conservation planning exercises that can be created as any of the area-based instruments that exist in Mexico. The total coverage of existing and proposed areas was calculated by merging the dataset to remove duplicates and dissolving the boundaries between polygons to determine the total area. Spatial analysis was carried out in ArcGIS using geoprocessing tools. Currently, more than 25% of Mexico’s marine area is legally protected or conserved, with federal marine protected areas covering more than 22% of the Exclusive Economic Zone. The legally established areas that can be considered OECMs cover about 3% of the marine territory. We found that more than 9% of Mexico’s Economic Exclusive Zone contains areas of high conservation importance that are not covered by any area-based instrument. This study shows that Mexico has the potential to protect or conserve 32.8% of its marine territory by 2030.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".