Analysis of noise and air pollution in Sambalpur City, India, during Diwali
Bibliographic record
Abstract
Diwali (or Deepavali) is one of the most glamorous cultural festivals in India. It is the festival of light and is celebrated every year during the month of November with great firework displays. Burning of different fireworks generates acute noise and toxic fumes, which lead to noise and air pollution in the environment. The excessive use of fireworks aggravates the level of air and noise pollution and causes adverse impacts on human health. The present paper assesses the noise pollution and air pollution during Diwali in Sambalpur City, one of the premier cities in the western part of Odisha State in Eastern India. The noise level is measured on Diwali day and is compared with that on normal days. Similarly, air quality is measured on Diwali day and compared with that on normal days (pre-Diwali and post-Diwali). The average equivalent continuous noise levels (L eq) and noise pollution levels have increased in all areas on Diwali compared with those on non-Diwali days. It is also observed that there is a significant rise of air pollutants, total suspended particulate matter, PM10, PM2.5, sulfur dioxide (SO2) and nitrogen dioxide (NO2) in ambient air due to burning of crackers in Diwali.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".