Bibliographic record
Abstract
This paper presents a general view about biomass production in India and its potential energy for use in different fields. India has tremendous potential for energy generation through biomass and its residues. Biomass energy is normally produced from firewood, agricultural residues such as bagasse, crop stalks, animal dung and wastes generated from agro-based industries. With the estimated and predicted values, the generating power from the surplus biomass in India was significant and it will continue to be more effective in future. Residue use as a fuel in India is estimated to be 216 Mt as projected value in 2010, recently, around 605 MW of electricity is being produced from biomass firing and 720 MW from cogeneration activities for residue. About 185 Mt (40%) of the dung collected is used as fuel in cook stoves. The potential for biogas production annually is 8750 million m3 from 251 Mt of dung. The amount of fuel-wood consumption during year 2004 was 205 million tonnes used as fuel for traditional cook stoves with low efficiency, 16 Mt used in industrial sector producing 10 PJ, and it was estimated that the production of fuel wood and charcoal increased to the rate of 1.98 per cent per annum. The total quantity of solid wastes generated in larger towns and cities has been estimated at 40 Mt in 2001, and in 2005 the average MSW generation in overall India was approximately 100,000 Mt/day. For the wastewater in India, in 2010, the energy estimated to be around 3929.8 TJ as energy value of CH4.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".