Transboundary Air Pollution in Malaysia: Impact and Perspective on Haze
Bibliographic record
Abstract
Modern technology is the invention of certain devices that improved people’s level of comfort as well as material goods to improve. Environmental pollution is often regarded as the product of modern technologies development and Haze is the brands of Air Pollution. Air Pollution means adding substances to the environment that do not belong to it and Haze is one of the signs that human has exceeded the limits. Air pollution is a gas released in a big enough quantity which contained poison gases to harm the health. One of the significant cases is the uncontrolled agriculture burning that happens in Indonesia annually which hindered activities of surrounding countries. The evidence shows that these activities give harm to land, environment and human’s life and also contribute to a global warming and cause for destruction of the only human Planet. A qualitative approach is employed to analyse the criticality level of pollutions that happened and to grab more understanding in Islamic perspective towards haze. The significant of this paper is to increase the public awareness about the effect of haze on world ecosystem and raise human sensitivity on environmental rights in Islamic perspective. This paper is focused on the negative impact of smoke pollution on the human life style and gives more ratifying on how Islamic laws views environmental calamity and how important the right of humans and environmental to not be violated and in addition to picture the importance of stabilizing the world so a prosperous life still be achieved by the next generation.Keywords: Air Pollution, Haze, Modern Technology, Negative Impact, Islamic Perspective
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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 | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".