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.
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 | 0.001 | 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 | 0.000 | 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".