The health and environmental impact of coal mining in Chhattisgarh
Bibliographic record
Abstract
"In spite of the fact that coal mining for coal-fired\npower generation is one the most hazardous\nand damaging industries such that governments\nof Austria, Belgium, Canada, Finland, France, New Zealand, Sweden, United Kingdom have pledged to phase out coal over the next decades, India, China, the United States and Russia continue to rely heavily on coal for the generation of electricity.\nIndia is one of the world’s major coal producers,\nranking third after China and the United States.\nSeveral national and international studies have\nestablished that the process of coal extraction,\nparticularly opencast mining, and electrical generation by coal-fired power plants release a range of gaseous and solid chemicals and heavy metals into the atmosphere as a by-product of this process.\nEvery step in the generation of electricity by coal-fired thermal power plants – the mining of coal, transportation, washing and preparation at the power plant, combustion and the disposal of post-combustion wastes carry serious risks on the health of miners, plant workers and residents in the vicinity of mines and power plants.\nExisting power plants in India, with few exceptions, are highly polluting- particularly as standards are only set for Particulate Matter (PM) rather than for all related pollutants including Sulphur dioxide (SO2), Nitrogen oxides (NOx) or heavy metals such as mercury. The PM standards are also lax. This research therefore crucially investigates the nature and impact of pollutants in air, soil, stream sediment and water on communities living\nclose to opencast mines and coal-fired power plants in Chhattisgarh."
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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".