Global Impact of Unconventional Energy Resources
Bibliographic record
Abstract
The chapters in this volume represent the latest thinking on the development and exploration of unconventional energy resources in the U.S., Canada, Australia, Europe, Russia, Asia Pacific, Middle East, Latin America, and Africa and shed light on its potential and future prospects in these respective regions. The diversity of thinking about the “shale revolution” is also evident in our case studies. Throughout many countries in Europe for example, there is a strong preference for investment in renewable sources of energy over the fossil fuels. In addition to environmental concerns, the falling price of renewables, have also made them more attractive financially. Consequently, global investment in renewables is outpacing that of fossil fuel two to one. Watching this trend, in 2017, the Chinese government has pledged to invest $360 billion on renewable energy. This would make China the largest investor in development of renewables in the world. Other obstacles to development of shale oil and gas in other parts of the world include, lack of adequate shale resources (Africa), the abundance of conventional energy resources (Middle East and North Africa), high cost of production (Russia, China, Japan) and political opposition to hydraulic fracturing (France and Poland). Despite these sentiments the economic imperatives (providing employment) also play a significant role in determining the future prospects for unconventional energy resources globally.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.003 |
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".