Bibliographic record
Abstract
Abstract Construction on peat soil has its own difficulty and challenge due to its properties of low shear strength, high compressibility, high water content, and flammable material. Indonesia ranked 4th on peat land area around the world after Canada, Russia, and United States. Based on recent mapping, peat land covers 14,9 million hectare in Indonesia. This paper presents the finding from literature studies regarding peat soil, construction on peat soil, case studies for road infrastructure construction on peat soil at West Borneo, Indonesia, and provide analysis and recommendation for the problems faced. Construction on peat soil requires special attention, especially on the design and execution. In the event where construction must be performed on a peatland which thickness is < 3m, the approach recommended is peat removal and replacement. If the peat thickness is 3-10 m, adopt the preloading, construction with vertical and sand drain, lightweight fills, surface mattress, stone column, etc. If the peat thickness is < 10m, use pile as foundation. Unit price contract is preferable over lumpsum contract due to the high uncertainties of peat soil. Environment impact due to construction on peat soil need to be accounted for, also the risk of peat fires need to be considered if dewatering used.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".