Achieving sustainable coastal environment in Langkawi, Malaysia
Bibliographic record
Abstract
Despite many good policies and institutions, the coastal environment of Langkawi continues to deteriorate. This could be due to lack of effective governance as well as unregulated waste discharge. Evidences collected from the literature during 1996 to 2013 also revealed a significant increase in the concentrations of Zn (R2 = 0.78) and Pb (R2 = 0.12) in the sediment. This appears to be the result of large volume of terrestrial runoff that brings these metals originating from extensive anthropogenic activities. It is a vital indicator of coastal pollution. It is a matter of concern that in many cases Pb concentration in the sediment exceeded the world average value 20 μg/g as well as Canadian Interim Sediment Quality Standard of 35 μg/g for the coastal areas. Similarly, the metal pollution index (MPI) measured over a period of 2007 to 2009 in fish also indicated an increasing trend of pollution in Langkawi. The maximum MPI value (4.87) was recorded in Spanish mackerel. Since pollution of coastal environment has serious implications for marine biodiversity and health of seafood consumers, measures are required to address this problem. Use of constructed wetland might be effective in reducing the coastal pollution as this will filter the effluent and waste before their mixing with the coastal water. Furthermore, enabling the stakeholders to play the environmental stewardship role will ensure better governance of coastal ecosystem and effective implementation of policies, envisaging an improved monitoring of waste/effluent discharge into the coastal marine environment. These measures are among the actions necessary for achieving a sustainable coastal environment of Langkawi.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".