Restoring and Managing Langat River Basin, Malaysia: Challenges for a Sustainable Future
Bibliographic record
Abstract
The project “Restoring and Managing Langat River Basin, Malaysia, for the Future” is an initiative carried out by LESTARI, UKM under the UNESCO framework of Sustainability Transformation across the Region (STAR) with Japanese Fund In Trust (JFIT) financial assistance. Sustainability science as a new emerging field can be a tool to solve a complex environmental anthropogenic issue by promoting an integrated approach of various disciplines, multiscale, and across stakeholder. It is a problem-driven and solution-oriented approach in creating a sustainable society and requires problem-solving skills. Thus, the establishment of sustainability science demo site in the Langat River Basin involved the integration of the sustainability science concepts into natural resource management frameworks and processes for supporting opportunities for a more sustainable and resilient future. This initiative is linked with Ecohydrology, HELP, and IWRM aspects and focused on applying sustainability science principles to strengthen policy, legal, and institutional frameworks through collaborative linkages, learning alliances, and targeted interventions for capacity building at river basin and national levels on urban stormwater management. The key objective of this sustainability science pilot project is to develop and implement a restoration and management plan for urban stormwater resources in the Langat River Basin using a sustainability science approach. Qualitative and quantitative data collection in the fields of hydrology, hydrogeology, pollution sources, ecosystems, and cultural preservation as well as development of the land cover-state model is done under this study. It is shown that the Langat River Basin is small but has inherited many problems of a large river basin. This is because the river plays an important role in conservation, agriculture, and potable water supply but is facing threat from rapid development in the industry sectors and urbanization in the basin. Some key strategies and action plans have been identified to deal with issues related to restoring and managing Langat River Basin for the future.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".