The Northeast Asian Seas: The Regional Legal Instruments of Cooperation for Marine Environment and Sustainable Development*
Bibliographic record
Abstract
The Asia Pacific region is characterized by a number of features which give prominence and unique significance in relation with the marine environment issues. Most people in the region live along the coasts, with one quarter of the world's 75 largest cities being near or on the region's coastlines. The marine resources of the region are economically important to most countries, with 47 % of world fisheries production being found in this region. The region is also the centre of global marine culture (87 % of total world production) with major consequences for coastal habitats and water quality1. As a result of rapid growth in population, economy, and political maturity, marine environment protection and resource management have emerged as a vital task for the individual nations, as well as the Asia Pacific region as a whole. The Asia Pacific region occupies only less than a quarter of the world land area, but has more than half of the total world population. This region is immensely diverse not only in the sense of economy, but also in view of environmental aspect. In combination with the papers in this panel analyzing the issues of other sub-regions, this study will examine some factors of marine environment and cooperation policy within the sub-region of the Northeast Asia. The Northeast Asian Seas encompass the Yellow Sea, the East China Sea and the East Sea the Sea of Japan2.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".