Mapping Out Vibrant Agricultural Communities In The Aizawl District
Bibliographic record
Abstract
Agriculture, however, continues to be the backbone of the Indian Economy. Significance of agriculture (though it contributes only 21 % to India’s GDP) in the country’s economic, social, and political fabric goes well beyond this indicator. The rural areas are still home to some 72 % of the India’s 1.25 billion people, many who are poor. Most of the rural poor depend on rain-fed agriculture and fragile forests for their livelihoods. The sharp rise in food grain production during India’s Green Revolution of the 1970s enabled the country to achieve self-sufficiency in food grains and stave off the threat of famine and food shortage. Agricultural intensification in the 1970s to 1980s also saw an increased demand for rural labour that raised rural wages and, together with declining food prices, reduced rural poverty. Agricultural growth since 1990s reduced rural poverty to 26.3 % by 1999-2000. Since then, however, the slowdown in agricultural growth has become a major cause for concern. India’s rice yields are one-third of China’s and about half of the yield in Vietnam and Indonesia. Except for sugarcane, potato and tea, the same is true for most other agricultural commodities. This requires a redefinition of agricultural efficiency at least in national context.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".