Prospects and directions of China's participation in the development and utilization of oil and gas resources in the Arctic
Bibliographic record
Abstract
The rich oil and gas resources in the Arctic have attracted worldwide attention in recent years. According to the academically accepted assessment by the United States Geological Survey, the Arctic oil and gas resources are unevenly distributed, mainly concentrated in Russia, Alaska (the USA), Norway, Canada and Greenland. The exploration and development of oil and gas resources show different characteristics in different countries and regions, which may change under new circumstances. China is a big consumer and importer of oil and gas resources, and the Russian Arctic Region is becoming one of the China's important oil and gas suppliers. This paper presents the current development and utilization status of Arctic oil and gas resources in major Arctic Nations and the future development prospects. Based on China's current participation in oil and gas development and utilization in the Arctic, this paper concludes by suggesting that China should take Russia as a long-term partner in the Arctic oil and gas resources development and the two sides should carry out cooperation in various fields, including project investment, technology investment and channel construction.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".