Structural Changes in India's Trade of Pulses: A Markov Approach
Bibliographic record
Abstract
To access the growth and structural changes taking place in pulses area, production, productivity and trade aspects this analysis was carried out for a period of 10 years ranging from 2007-08 to 2016-17. It was done by calculating compound and simple growth statistics and by developing separate transitional matrix for exports and imports. Results regarding area, production, productivity, collective quantity and value of exports exhibited 1.88 percent, 3.63 percent, 1.71 percent,5.45 percent and 3.23 percent rate of compound growth rate respectively. Markov Chain analysis was attempted through linear programming method to assess the transition probabilities for the major pulses export markets of Indian pulses and nations importing pulses to India. The major Indian pulses export markets were categorized as stable market (UAE) and unstable markets (Pakistan & Saudi Arab) based on the magnitude of transition probabilities. The import transition matrix also brought forward Canada and Myanmar as most trusted and Australia & USA as most unstable suppliers of Indian pulses imports. In conclusion, the growth in production (3.63 %) of the world's largest producer was far behind the simultaneous growth in import quantity (17.12%).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".