Global Shale Revolution: Successes, Challenges, and Prospects
Bibliographic record
Abstract
This study reveals the current problems and prospects of developing shale oil and gas industries in the USA, Canada, Mexico, Poland, Russia, China, India, and Australia. This approach allows a comprehensive and wide view on the industry and its geography. A brief review of the technologies implemented in the shale industry is provided. The key aim of the paper is to compare the hydrocarbon market conjuncture and economic environment (including financial), in the above-mentioned states, in order to reveal the factors contributing to the development of the industry. The methodology is based on the statistical estimation of the extraction, exports, and reserves of extractable shale hydrocarbons. The analysis given allows the forecast and estimation of the economic effects and external institutional effects of shale hydrocarbon extraction. It also contributes to the evaluation of the prospects of shale industry development in America, the EU, Russia, and the Asia-Pacific region. In accordance with the overall impact the shale revolution has had on the economies, environmental conditions, and societies of the chosen countries, recommendations are provided. The authors develop three scenarios for the future of the shale industry. The most probable scenario is a slower dissemination of horizontal drilling, as well as tight oil and shale gas extraction, with the decline of conventional reserve volumes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".