Bibliographic record
Abstract
One of the main objectives of the governments’ export policy is to maximise agricultural exports in order to earn foreign exchange. It also seeks to provide remunerative prices to the farmers while ensuring adequate availability of essential commodities to the domestic consumers at reasonable prices. This paper analyses the India’s exports of livestock and allied products to principal groups of countries in the light of world trade. The direction of trade has been analysed to explore the areas where exports can be given a further boost. Four groups of commodities -1) Live animals, 2) meat and meat preparations, 3) milk and milk products and 4) eggs, honey and other products of animal origin- have been selected for the analysis. Harmonised system of nomenclature classifies the commodities on the basis of origin, use, functions and trade. Exports of select groups of commodities over 10 years have been analysed from the period 1993-1994 to 2002-2003 (in short 1993 to 2002). Importers of Indian products have been arranged in 5 groups- SAARC includes Bangladesh, Bhutan, Maldives, Nepal, Pakistan, and Sri Lanka. Middle East Group includes UAE, Saudi Arabia, Kuwait, Yemen, Bahrain, Turkey, Qatar, Lebanon, Iran, Iraq, and Israel. High Income Asian Countries (HIAC) includes Japan, Singapore, South Korea, Taiwan, China, Hong Kong, Thailand, Indonesia, Malaysia and Philippines. High Income Other Countries (HIOC) include Australia, Austria, Belgium, Canada, Denmark, Germany, Iceland, Ireland, Israel, Netherlands, Portugal, Spain, Switzerland, UK and USA, Rest of World is the fifth group. No country has been repeated in any group.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".