REALTIONSHIP BETWEEN GREEN HOUSE GAS EMISSION AND PRODUCTIVITY OF FOOD GRAINS IN INDIA -2000-2016
Bibliographic record
Abstract
The green house gas emission includes carbon emission, nitrous oxide emission and methane emission. The above green house gases are the major factors for climate change. The top 15 countries which generate carbon emission are China, United States, India, Iron, Saudi Arabia, South Korea, Canada, Mexico, Indonesia, Brazil, South Africa and Turkey in the world economies. In 2017, China is the first important country in emitting the carbon di oxide. It alone emitted 9839 metric tonnes in 2017. The share of China in the world carbon emission was 27.2 percentage in 2017. Next to China, United states was the second important country which emitted 5269 metric tonnes in 2017. The share of United States in the total carbon emission was the 14.6 percentage. India emitted 2467 metric tonnes. The share of India in the world emission was 6.8 percentage. It showed that India was the third largest country in emitting the carbon di oxide followed by Iron, Saudi Arabia, South Korea, Canada, Mexico, Indonesia, Brazil, South Africa and Turkey (World Bank, 2018). The green house gases are the major factors contributing for crop growth and food grains production. The studies(Yinhong Kang,Shahbaz Khan ,Xiaoyi Ma 2009 ) are attempted to assess the relationship between green house gas emission and food grains productivity in various time period. In this back drop, an attempt is made to assess the impact of green house gases on food grains productivity in India. The findings of the study show that *The green house gas emission has shown an increasing trend. * The growth of carbon emission is higher than the other green house gases. *The relationship between carbon emission and food grains productivity is statistically significant. The increase in carbon emission would increase the productivity of food grains only up to a certain stage, beyond that, the increase in carbon emission would reduce the productivity of food grains.
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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 | 0.000 |
| 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".