Characterization of Leachate from Kuala Sepetang and Kulim Landfills: A Comparative Study
Bibliographic record
Abstract
The general characteristics of Kuala Sepetang Landfill Site (KSLS) and Kulim Landfill Site (KLS) in the northern part of Malaysia were investigated. The average values of the parameters for leachate at KSLS and KLS, such as pH (8.05 and 7.59), EC (11.9 and 2.92 mS/cm), ORP (-33.02 and +17.8 mV), turbidity (88.9 and 26 NTU), colour (2200 and 326 Pt Co), SS (233 and 47 mg/L), BOD5 (158 and 29 mg/L), COD (855 and 117 mg/L), BOD5/COD (0.19 and 0.24), ammonia-N (857 and 210 mg/L), sulphate (91.48 and 141.71 mg/L), chloride (1800.46 and 243.18 mg/L), copper (0.08 and 0.03 mg/L), iron (2.18 and 0.38 mg/L), manganese (0.08 and 0.09 mg/L), nickel (0.16 and 0,07 mg/L), and zinc (0.26 and 0.09), were recorded, respectively. Results of this study indicate that KSLS and KLS had low refractory (BOD5/COD) organic compounds and high quantities of COD and ammonia-N. Furthermore, the amounts of colour, suspended solid (SS), BOD5,COD, ammonia-N, and sulphate exceeded the standard limits imposed by the Environmental Quality (Control of Pollution from Solid Waste Transfer Station and Landfill) Regulations 2009, Malaysian Environmental Quality Act 1974 (Act 127). On the other hand, parameters such as pH, copper, iron, manganese, nickel, and zinc remained within the allowable limits. The measured leachate would need an appropriate treatment strategy to reduce the pollutants to a satisfactory level prior to discharge into receiving system.
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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 | 0.000 | 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".