Wheat Production in India: Technologies to Face Future Challenges
Bibliographic record
Abstract
To meet the growing demands under the constrains of depleting natural resources, environmental fluctuation andincreased risk of epidemic outbreak, the task of increasing wheat production has become daunting. The euphoriagenerated by first green revolution is very quickly subsiding and the second generation problems are becomingmore intense with each passing year. The factors responsible for first green revolution seem to be exhaustingrapidly and there is immediate need to develop the technologies which can not only increase the wheatproduction but also sustain at higher level without adversely affecting the natural resources. More investment ongermplasm improvement, conservation agriculture including breeding for varieties adaptive to conservationagriculture, hybrid wheats, broadening the genetic base of the varieties at farmers level, wide scale utilization ofalien translocations in the breeding programme along with integration of marker assisted selection and otherinnovative approaches with traditional breeding methods are some of the technologies which can yield dividendin the coming years.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".