Bibliographic record
Abstract
This article aims to study on the trend of international legislation on the changing fisheries in the high seas of the Central Arctic. In the Arctic Ocean, there have been a lot of environmental changes including the fishery environment due to global climate change. In the Arctic, fishing grounds has been formed with Barents Sea in the North Atlantic and Bering Sea in the North Pacific. However, there has been a strong possibility of fisheries in the high seas of the Central Arctic as the thawing of the Arctic ice slows down recently. For such reasons, five(5) Arctic coastal States held three times inter-governmental meetings on fisheries in the high seas of the Central Arctic: Oslo in 2010, Washington in 2013 and Nuuk in 2014. In July, 2015, five(5) Arctic coastal States announced the Declaration Concerning the prevention of Unregulated High Seas Fishing in the Central Arctic Ocean(hereinafter “Oslo Declaration”) on the authority of precautionary approaches. With this Declaration, the US convened a meeting to make a draft on the Agreement to Prevent Unregulated High Seas Fisheries in the Central Arctic Ocean on December 2015. And they are consistently hold the meeting on high seas fishing in the central arctic ocean; the December 2015 Washington DC Meeting, the April 2016 Washington DC Meeting and the July 2016 Iqaluit Meeting. These meeting are unique in that it was held together with Scientific Workshops. Besides, they are operated with “broader process” and governments of the Republic of Korea, Japan, China, Iceland and EU participated. It may be significant that Korea as a major distant water fishery state reviews the trend of international legislation in the high seas of the Central Arctic as well. This article reviews and analyses Oslo Declaration and a draft on the Agreement to Prevent Unregulated High Seas Fisheries in the Central Arctic Ocean. Furthermore, it recommends some point for the position the Korean government should take for the Arctic Ocean.
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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".