Bibliographic record
Abstract
1980년 중반에 개발된 석탄층메탄가스(Coalbed Methane) 개발기술은 석탄층에 흡착된 메탄가스를 생산할 수 있는 기술이다. CBM은 개발이 쉽고 매장량도 풍부하다. 따라서 CBM 산업은 온실가스 배출규제에 대처할 수 있을 뿐만 아니라 에너지자원으로서의 잠재력이 매우 크다. 석탄을 개발하기 위해서는 메탄가스 폭발에 대비한 선행적 광산 보안조치로 CBM을 개발해야 한다. 그렇기 때문에 가스시장의 변동에 영향을 받지 않는 장점이 있을 뿐더러 지구온실가스 감축에도 CBM은 유리한 입장에 있다. ECBM(Enhanced Coalbed Methane)은 석탄층 메탄가스의 새로운 생산 기법으로 석탄층에 <TEX>$CO_2$</TEX>나 <TEX>$N_2$</TEX> 가스를 주입하여 석탄에 흡착된 메탄가스를 탈착시켜 생산하는 방법이다. 특히 <TEX>$CO_2$</TEX>-ECBM 공법은 저탄소 녹색성장 기술로서 메탄가스의 생산성 향상뿐 만 아니라 온실가스 저감 효과도 기대할 수 있다. CBM개발은 캐나다, 호주, 중국, 인도, 인도네시아, 베트남 등 40여개 국가에서 개발이 진행되고 있고 생산량이 꾸준히 증가하고 있다. 현재의 석탄-석유 에너지원에서 비전통 가스로의 에너지 패러다임 전환에 CBM이 일조할 전망이다. The CBM(Coalbed Methane) development technology being developed in mid 1980s is the technology to produce the methane gas absorbed in the coal bed. CBM is easy to be developed and its coal deposit is abundant. Therefore, the CBM industry has a large potential as an energy source as well as to deal with the global regulations for reducing greenhouse gas emission. In order to produce coal, the CBM should first be developed as a preliminary action for mine security. So CBM is advantageous in reducing the global greenhouse gas as well as its advantage not being influenced by the changes in gas market. The ECBM (Enhanced Coalbed Methane) is a new technique producing the methane gas which is substituted and disorbed from coal by injecting <TEX>$CO_2$</TEX> or <TEX>$N_2$</TEX> gas into a coal bed. Especially, <TEX>$CO_2$</TEX>-ECMB is a low-carbon, green-growth technology, so can expect to the effect of green gas reduction as well as the improved productivity of methane gas. CBM technology is being developed in about 40 nations including Canada, Australia, China, India, Indonesia and Viet Nam, and the coal output using this technology is continually being increased. The CBM is expected to contribute in changing the energy source paradigm from current coal & petroleum energy to unconventional gas.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".