Analysis of air quality using ensemble technique
Bibliographic record
Abstract
Air is one of the most essential resources for life, and monitoring and protecting its quality has become a major priority for governments in both urban and rural areas.Activities like transportation, burning fossil fuels, and construction contribute significantly to air pollution.In this study, we focus on analyzing the concentration of sulfur dioxide (SO2) in the air in two regions: Delhi and Gujarat.Using advanced machine learning techniques, we apply ensemble methods to measure the levels of SO2 in these areas, with time series analysis to track periodic changes over time.We used Random Forest regression to compare SO2 levels in Delhi and Gujarat.We found that the model for predicting SO2 levels in Delhi showed a slightly better fit than the model for Gujarat.This project provides valuable insights into air quality, raising awareness about the harmful effects of air pollution on human health.It also offers useful data for environmentalists and policymakers to help shape regulations that address air quality concerns and reduce exposure to toxic pollutants.By understanding SO2 concentration trends, this analysis contributes to ongoing efforts to improve air quality and safeguard public health in the future.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".