Health and environmental consequences of crop residue burning correlated with increasing crop yields midst India’s Green Revolution
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The Green Revolution (GR) enhances crop yields significantly that contributes greatly to the social and economic development of many less developed countries. However, the increasing crop yields might rise crop residue biomass burning, leading to adverse environmental and health consequences. We assess the impact of crop residue burning associated with the GR-induced growing crop yields on benzo[a]pyrene (BaP) pollution, a congener of polycyclic aromatic hydrocarbons with strong carcinogenicity. We find a significant increasing trend of BaP emission and contamination from crop residue biomass burning from the mid-1960s to 2010s in India, coinciding with the growing crop yields occurring during the GR. Our results reveal that agricultural BaP emission driven lifetime lung cancer risk (ILCR) in India increased 2.6 times from the onset of GR in the mid-1960s to 2014 and the 57% population in India was exposed to the BaP level higher than the India national standard (1 ng m −3 ). We show that the reduction of open crop residue burning during the rice and wheat residue burning period would be a very effective measure to reduce BaP environmental contamination and health risk.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| 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 it