Geographical and Legal Problems of Delimitation of the Arctic Territories in the Krasnoyarsk Territory (Krai)
Bibliographic record
Abstract
The present study deals with the geographical and legal problems of delimitation of the North and Arctic territories in the Krasnoyarsk Territory (Krai).With respect to the legislative delimitation of the North as a separate entity, it provides characteristics the three main latitude zones of the Krasnoyarsk Territory (Krai): the South (the Southern Latitude Belt), the Near North, and the Far (Extreme) North, along with the respective principles of territorial policy.The emphasis is placed on the insufficient substantiation of the "Arctic zone" delimitation on land, especially on the existing contradictions between its boundaries and the boundaries of the Extreme North zone.The article also explores the problem of political and legal regulation as regards the demarcation of the water area of the Arctic Ocean, which arose after the entry of the 1982 UN Convention on the Law of the Sea (UNCLOS) into force, as its principal provisions were fundamentally different from the historically established division of the Arctic into five polar sectors belonging to Russia, Canada, the USA, Denmark and Norway.Some issues related to the influence of the differences in the Arctic maritime spaces' legal status on the peculiarities of the development of their natural resources are also covered.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".