Spatial Distribution of Noise Released from Iron and Steel Industry and their Effects on Human Health in the Lahore City, Pakistan
Bibliographic record
Abstract
This research investigates the phenomenon of perception of people about industrial noise pollution and its effects on human health. Thirty-six (36) industries were selected for estimation of noise levels and its effects on human health. Concurrently, samples of one hundred and fifty (150) respondents were also taken from nearby residential area, using random sampling method. The key tool of data collection was well-structured questionnaires consisting of twenty-one questions. Chi-Square test was used for examination of data, which illustrated effects of industrial noise on people living in industrial zone. The noise level results indicated that the mean values were exceeding permissible environmental standard used in Pakistan. Majority of respondents (50.6 %) were conscious about the basic reason of noise pollution in study area. Eighty-two percent (82%) people have opinion that old technology was the basic cause for noise pollution. It was shocking to see the results which indicate that 99.8% people are suffering from noise related diseases. This include 81.3% with increase anger, 81.5% with ear ache, 16% with ear discharge, 79.3% with high blood pressure, 78% with depression, 77.3% temporary hearing loss, 9.3% permanent hearing loss. Only 23.3% of people conduct regular hearing test.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".