Emerging challenges and issues of pulses imports in India
Bibliographic record
Abstract
The size of Pulses economy of the world is 61.3 million MT. India is the largest producing country with 22 % (13.50 million MT) of the world production concentrated in India. But, as India has a large vegetarian population, which is largely dependent upon pulses, wheat and milk as its major source of protein, the size of consumption of pulses in India is around 16 million MT. In order to meet such demand, India is dependent upon import of pulses to the extent of 23 million MT. India imports its requirements from various countries, such as Myanmar (Urad & Tur), Canada,Australia and various other countries. The Paper presented tries to review the current import policy, the tendering mechanism presently by the Indian Government & suggests some direction for policies to government of India in import of pulses, which will definitely help to frame out a long term strategy in import of pulses & also assure to meet pulses demand of the country with stabilizing the rising prices of pulses in Indian market giving relief to the Indian consumer.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".