An Analysis of Oilseeds and Pulses Scenario in Eastern India during 2050-51
Bibliographic record
Abstract
India is no longer dependent on other land produce especially with respect to food grains, thanks goes to policy planner researcher and the most to our farmers who make this dream to happen in our life time. We have to have worked hard systematically to prove our credential with regards to oilseeds and pulses. India is the world leader in production, consumption and import as well in case of pulses and not for behind in vegetable oils. India imports 2-3 Mt of pulses on regular basis and 9.2 Mt of vegetable oils during 2010-11.Currently India is in the mid-way of self-sustaining in oilseeds and pulses production. By the 2050, India as a whole will be able to sustain their production. Indian will produced plenty with respect to both the non-food commodity i.e. oilseeds and pulses. India may emerged as net exporter from being net importer for century the with respect to oilseed, and most probably for pulses also. Keeping in mind the socio-political situation and poor land to man ratio in eastern region the likelihood for oilseeds and pulses is not at all in bad shape. Inclusion of soybean and summer sesamum as oilseeds crop, nonconventional legume like faba bean and cowpea and summer mungbean in irrigated condition, cropping system approach especially intercropping are one of few options for horizontal expansion coupled with several technologies niche for vertical acceleration of production and productivity.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".