Sustainable Development, Electricity Generation and Renewable Energy substitution in Middle East Countries and Cooperation of Iran
Bibliographic record
Abstract
The energy category over the world has been considered as Coal, Natural Gas, Hydro, Nuclear, Wind, Oil and Solar PV by means of 38%; 29%, 16%; 10%and %5 respectively. Where the main reserve holders of Russia, Iran, Qatar, U.S.A. and Saudi Arabia r, have regarded natural gas as an alternative of energy. In which Iran is in the second position after Russia. Where the electricity generation has been considered by China, U.S.A, India, Russia, Japan and Canada respectively and Iran is in the 14th place. Besides renewable energy, capacity installed over the world is in terms of GW-2019 has been defined by China, U.S.A, and Brazil, India, and Germany respectively. However, solar power installed in Middle East Countries and Africa (MENA) is in terms of Terra watt-hour per year by Algeria, Libya, Saudi Arabia, Egypt, Morocco and Tunisia. Whereas solar power markets has been classified by China, India, Japan, Australia, Mexico, Turkey and Netherland in GW-2018 respectively Iran has been considered for the energy resource of Oil as 25%, Natural Gas as 69%, and Hydroelectricity as 6% in 2018 as the main respectively. China, U.S.A., India, Japan and Australia have respected the solar power market consideration in GW-2018 as the most important countries. Other considerations for CO2 emission over the globe has been made by China (27.2%), U.S.A (14.58%), India (6.82%), Russia (4.68%), Japan (3.33%) and, Germany (2.21%) as the most important ones.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".