Overview of the Sustainable Uses of Peat Soil in Malaysia with Some Relevant Geotechnical Assessments
Bibliographic record
Abstract
Peat soil is an important ecosystem that provides a significant contribution to the global climate stability. In Malaysia, peat soils are considered as a soil with little economic benefit, apart from it being used for agricultural activity. The total world coverage of peat soil is about thirty million hectares with Canada and Russia having the largest distribution of peat (Zainorabiddin,2010). More than sixty percent of the world’s tropical peat lands are found in South-East Asia (Lette,2006). Most notable are the large peat land on the islands of Borneo (belonging to Indonesia, Malaysia and Brunei) and Sumatra (Indonesia). However, there are also significant occurrences in other parts of Indonesia, Malaysia, Vietnam, Thailand and the Philippines. The main contributory functions and benefits of peat soil are within the engineering disciplines of hydrology, agriculture, social-economics, biodiversity habitats and carbon sequestration. Peat was used in temperate climates (especially in Finland, Ireland, Sweden and UK) as a fuel to generate electricity and heat. Therefore peat can be considered as a renewable energy source but this will be very detrimental to the market of genuine renewables. The western coastal lowlands of Malaysia (such as Kukup) are mangroves that represent the initiation of peat soil formation. Such areas provide the natural habitat of mangrove forests. It also fixes more carbon from the atmosphere than is released and approximately one-quarter of the carbon stored in land plants and soils. On the other hand, peat is one of the problematic or challenging foundation soil of poor quality due to it’s very high water content, high compressibility and low shear strength. Peat consists of decomposed plant fragments and the unfavourable characteristics of peat soil deposits make them unsuitable for making sustainable infrastructure development for varied engineering projects. This paper therefore gives an overview of the pros and cons debate of sustainability aspects and in the light of the challenges it poses to infrastructure development 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".