Analysis of the ocean and marine health performances of 18 countries in the G20 countries: An application using the CEBM-based TOPSIS method
Bibliographic record
Abstract
This study investigates the critical role of G20 nations in maintaining ocean health, given the significant influence their economic activities have on global maritime ecosystems. Employing the most recent Ocean Health Index (OHI) data (2023) and the CEBM-TOPSIS Multi-Criteria Decision Making (MCDM) method, the research assesses the ocean health performance of 18 countries G20 countries. The CEBM analysis identifies biodiversity, carbon sequestration capacity, fisheries sustainability, water quality, and coastal protection as the most important OHI criteria, respectively. According to the CEBM-TOPSIS method, Russia, Brazil, and France are the top three countries with the highest ocean health performance, while China, India, and South Africa are ranked lowest among the first three countries. Notably, the average performance score indicates that Russia, Brazil, France, the United Kingdom, Australia, Mexico, South Korea, the United States, Germany, Saudi Arabia, and Canada all exceed the average. This suggests a need for improvement among G20 countries with below-average performance to ensure a more substantial contribution to the global economy and interconnected dimensions. Finally, sensitivity, comparison, and simulation analysis validate the CEBM-TOPSIS MCDM method as a reliable tool for evaluating national ocean health performance.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".