Advanced Strategies to Mobilize Crop Residue to Replace Coal in India
Bibliographic record
Abstract
Various published data show the amount of crop residue available annually in India may range from a low of 90 to a high of 180 million tonnes. Different types of crop residue are collected from farmers depending on the geography and crop pattern for instance, in north India rice straw and cotton stalks are collected while in central India soya husk and sugarcane tops are collected. Baling and transporting straw from the field, though appear to be an option for safe disposal, will be feasible only when alternate, effective and economically viable usage methods are identified and facilities and infrastructure for ex-situ management methods are created. One immediate short term use of the residue is to replace 5% - 7% of the 670 million tonnes of coal India currently consumes to generate power. The farmers will benefit from the sale of their excess crop residue. The scheme will reduce pollution due to residue burning practices. Replacing coal will cut the GHG emissions. The challenge is to mobilize the crop residue collection and timely delivery to power plants. The data and calculations in this monogram show that it is economical for the farmer to remove the crop residue from the field quickly by using modern balers, to pelletize the biomass in small-scale distributed pellet plants, to store pellets in the modern steel bins and finally to deliver the pellets to coal plants by using rail transport. The delivered cost is estimated at around Rp 6.78/kg. The Government of India encourages the power plants to pay at least Rp 10/kg for the delivered biomass in the form of pellets. The current monogram analyzes the organization of an efficient supply chain in the State of Haryana India to ensure a sustainable modern enterprise.
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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 | 0.001 | 0.002 |
| 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".