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Record W7098909922

The Northeast Asian Seas: The Regional Legal Instruments of Cooperation for Marine Environment and Sustainable Development*

2000· article· en· W7098909922 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Taxonomy and Phylogenetics
Canadian institutionsnot available
Fundersnot available
KeywordsChinaQuarter (Canadian coin)Resource (disambiguation)Marine habitatsMarine conservationSustainable developmentEast AsiaHabitat
DOInot available

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.976
Threshold uncertainty score0.344

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.169
Teacher spread0.157 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2000
Admission routes1
Has abstractyes

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