ENERGY DEMAND FORECAST FOR TURKISH AGRICULTURE SECTOR: GRANGER CAUSALITY AND COINTEGRATION TEST
Bibliographic record
Abstract
Due to the fact that, Turkey is a importing energy, we must determine the energy needs in the Turkish agricultural sector. In this study, consumed energy data in agriculture was used between 1972 and 2015 years. According to Turkish Statistical Institute’s database, agriculture sector shares in GDP 6.2 percent in 2016 and percentage change compared to same period in previous year -0.1 percent. Agriculture sector shares in GDP 6.1 percent in 2017 and percentage change compared to same period in previous year 17.2 percent. Gross domestic product increased by 5.2% compared with the same quarter of the previous year in the second quarter of 2018. When the activities which constitute gross domestic product were analysed the total value added decreased by 1.5% in the agricultural sector compared with the same quarter of the previous year in the chained linked volume index. Trend model was used to energy trend in the econometric analysis of this study. Granger causality analysis results show that one-way causality relation at 5% level of significance towards GDP denoted EC was detected.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".