Factors Influence Tea Exports in North Sumatera Province
Bibliographic record
Abstract
- The province of North SumatEra has a leading commodity tea that shows the role in international trade activities through exports to several countries in the world. This study aims to analyze the effect of production, GDP of destination country, population of destination country, and exchange rate against dollar against tea export of North Sumatera. The type of this research is quantitative analysis using time series data from 2006 to 2015 from 10 export destination countries, namely Malaysia, United States, United Kingdom, Taiwan, Germany, Singapore, Pakistan, Emirates Arab, Canada and Russia. Data obtained from Central Bureau of Statistics (BPS) of North Sumatra Province and World Bank. Data analysis technique used is panel data regression with Fixed Effect Model (FEM) model. The results showed that production and GDP had positive and significant effect, the number of population had negative and significant effect, while the exchange rate did not significantly influence the tea export of North Sumatera.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".