TURKEY'S RENEWABLE ENERGY OUTLOOK AND A GENERAL ASSESSMENT OF RECENT DEVELOPMENTS
Bibliographic record
Abstract
Turkey is the world's 17th and Europe's 6th largest economy and the country has made significant advances and breakthroughs in its economy since last two decades. Turkey is a very rich country in terms of geographical location and geological structure due to renewable energy sources. The country has planning to take advantage of these resources to the maximum extent aiming both will contribute to security of energy supply and prepare the ground for the creation of new jobs. The installed power of renewable energy sources, which was 12,305 MW in 2002, has reached the value of 36,702 MW in the third quarter of 2017 with an increase of 198%, approximately. This is an indicator that the renewable energy can be a solution to achieve the country's future goals of energy. This paper presents an overview of Turkey's renewable energy appearance and a general examination of renewable energy status with the latest figures, which determined according to the recent developments. It is also aimed to contribute to all fields, businesses and the industry working on renewable energy, not only to whom seeking the newest developments and latest numbers, but also planning to produce green energy and create green jobs for the country's sustainable development.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".