A review of air pollutant index in Selangor for 2011
Bibliographic record
Abstract
Now days one of the biggest threat in term of environment in Malaysia is Air Pollution but the air quality level in Malaysia is not bad as other countries like Canada, China and India. Department of Environment (DOE) has established Air Pollutant Index (API) which describe about the ambient air quality measurement in Malaysia and this API is developed easily in understood range from the status of good to hazardous. The purpose of this study is to review the trend of air pollution in Selangor for 2011 which specifically involved five of different places monitoring station. A graph for all the type of parameter like SO₂, NO₂, PM₁₀, CO and O₃ will be plotted against time. Based on this research the suitable relationship for all the parameter with the logically causes that contribute toward the trend concentration need to be determined. Based on analysis of the trend, it can be observed that, the most contribute pollutant toward the air quality in Selangor was PM10 followed by CO, O₃, NO₂ and SO₂. Based on the result also, the most populated places in Selangor was observed in Klang followed by Petaling Jaya, Shah Alam, Banting and Kuala Selangor. When comparing with the MAAQG, every type of pollutant still not exceed the limit of level except the PM10 at some particular period of time. For the best of recommendation, this type of analysis can be used for the planner in order to develop more study in term air pollution and also investigate the best relationship between of meteorological condition with the air quality which can explore more understanding toward the air quality in Malaysia.
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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 | 0.009 | 0.011 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".