Bibliographic record
Abstract
While conventional oil and gas production is expected to hit a peak in dozens of years, the world still requires hydrocarbons as the main source of energy. The need to fill the gap between supply and demand, and the recent high oil price, have aroused enthusiasm for unconventional oil and gas. But, the development of them is, full of ups and downs.The extra heavy oil boom began in early 2000's, when large projects started in Canada and Venezuela. But, rising plant cost, shortage of skilled workers, and environmental concerns have increased uncertainty of those projects, and finally this year global economic crunch and oil price plunge ended the boom. In those circumstances, technologies to reduce production cost are keenly craved and several are on trial in actual projects.In 2005, U.S. Congress directed the Bureau of Land Management to manage oil shale development on public lands. By 2008, the bureau issued six research development and demonstration leases that will allow oil shale development to resume on a 160-acre tract of public land. Currently, in-situ conversion process is arguably the most cost effective recovery method.The U.S. has led the world in coalbed methane (CBM) development, and the production reached 1.8 tcf by 2006. While some countries are after the footsteps of the U.S. in CBM development, the U.S. is now considering giving a higher priority to shale gas instead as the results of efforts to develop economical production methods for years.Japan has studied gas hydrate development since 1990's. In 2008, the Japan Oil, Gas and Metals National Corporation carried out a successful gas hydrate production test in Canada. The next step of their pursuit will include practical application of the production method, nationwide potential evaluation, and development of environmental assessment technique.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".