A Study on Growth and Direction of Black Pepper Trade in India-A Markov Chain Approach
Bibliographic record
Abstract
India is the second largest producer of black pepper in the world production. The export of black pepper in India has increased from 74 crores in 1991–92 to 878 crores in 2011–12. Compound growth rate (CGR) was used for analyzing the growth in black pepper area, production, yield, export quantity, unit value and export value over the years. The results indicated that the growth rate of area, production, productivity and unit value were found higher during pre-liberalization period than post-liberalization oroverall period due to the stiff competition from different black pepper, producing countries, which lead to decline in the growth rate. The Markov chain analysis revealed that the major Indian black pepper export markets, were categorized as stable market (USA, Germany, UK, Italy, Canada and other category) based on the magnitude of transition probabilities. The data regarding country-wise export of black pepper has showed that the previousexport share retention for Indian black pepper has been high in minor importing countries (pooled under others category) (85%), followed by USA (78%), Germany (41%), Italy (33%), Canada (16%) and UK (11%). The increasing share of other countries clearly showed that the need to explore and exploit the market potential of other countries. Efforts are also needed to improve the efficiency of production to make the product acceptable and price competitive in other importing countries.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".