IMPROVING IRRIGATION IN INDIA: THE NIZLECTED OPPORTUNITY
Bibliographic record
Abstract
Over the centuries sizable investments have been made in India to develop the irrigation potential and these investments have continued during the first quarter century of independence. By 1968-69 the net irrigated area was 71 million acres or about 21 percent of the net area sown. The 1968-69 level of irrigation is 17 percent above the 1960-61 level and 38 percent greater than in 1950-51. However, there is wide variation in the type and quality of irrigation with over a third of the irrigation coming from government canals, 17 percent from small reservoirs (tanks),8 percent from tube-wells, and the remainder from other wells and private canals. With the advent of high yielding varieties (HYV’s) of wheat and the increased use of fertilizer, the returns to irrigationwater increased sharply and led to a rapid expansion of private tube-well irrigation, particularly in Northwestern India. The more recent spread of HYV’S of rice and the continued population pressure have pushed up the returns to
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".