Bibliographic record
Abstract
중국 정부와 산하기관들은 자국의 불안정한 자원수급을 위해 노력하고 있다. 중국 국토자원부(MNR)는 광물에너지자원 잠재력과 가채매장량 평가를 위해 중국 국토자원규획(1999~2010), 중국 광산자원조사규획(2008~2020), 중국 셰일가스산업정책 공고(2013), 중국 셰일가스 자원평가 및 우선 개발지역 선정 프로젝트(2012), 중국 셰일가스 개발규획(2011~2015) 등과 같은 국가 차원의 시책을 시행하였다. 중국의 셰일가스 자원은 국가 매장량의 대부분으로 평가된 우수한 잠재력을 가진 쓰촨분지와 타림분지, 2개의 거대 퇴적분지는 있는데, 이미 전 국토에 걸쳐 셰일가스 가채 매장량이 광범위하게 분포하는 것으로 조사되었다. 중국의 셰일가스 가채 매장량 규모는 31조 $m^3$ (1,115조 cubic feet) 정도로 평가되고 있으며, 중국의 미국, 캐나다와 함께 세계 3대 셰일가스 상업생산이 가능한 국가 중의 하나이다. 지금 중국은 셰일가스의 상업생산을 증진하기 위한 기술개발과 가채 매장량의 확충을 위한 조사 탐사활동에 매진하고 있다. 중국의 이러한 정책과 개발 관련 기술분석을 토대로 할 때, 우리는 국제 유가시장 변동 등에 따른 중국의 셰일가스 개발과 R&D 동향을 적극적으로 모니터링 되어야 한다고 사료된다. 【The Chinese government and its agencies were trying in order to solve the unstability of resource supply and demand. Ministry of Land and Resources of China(MLR) carried out a lot of national-level policy and planning for estimating the domestic mineral and energy resources potential and recoverable reserves, as the Chinese land and resources survey plan(1999~2010), the Chinese mineral resource survey and exploration plan(2008~2020), announcement for shale gas industry policies of China, the Chinese shale gas resources evaluation and selection project for its development priority areas(2012), and the plan for Chinese shale gas development(2011~2015). The two large sedimentary basins of Chinese shale gas reserves are Sichuan and Tarim basins with excellent potential, accounting for majority of the estimated national reserves. Recoverable gas-bearing shale of China was surveyed to be widespread. The volume of recoverable shale gas reservoirs in China has been estimated to be around 31 trillion cubic meters(1,115 trillion cubic feet). China is one of only three countries with the US and Canada to produce shale gas in commercial quantities. China is concentrating on technology development to enhance commercial production of shale gas, and on survey and exploration activities to increase its recoverable reserves. The trends related to shale gas development and R&D activities in China to respond to changes in international oil market should be actively monitored based on analysis of Chinese policies and technology.】
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.007 |
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".