Growth Morphology of Settlement on the Riverside Musi in Palembang
Bibliographic record
Abstract
The growth of settlements on the banks of the Musi River, Palembang as a center of trade and government cannot be separated from the existence of rivers and their flows which then change the morphology of the riverbanks. The morphological development of the city formed as a result of riverbank reclamation is strongly influenced by endogenous and exogenous forces. Physical development by the community that is not in harmony with the river causes environmental problems that affect the quality of the residential environment. The research method uses a qualitative case study approach to explore information on the reality of the field in the research area. Each segment is analyzed for settlement growth which is formulated into four categories and then produces a theme. Overall, the morphology of the settlement growth of the Musi Riverside is in two patterns, namely with urban growth where the riverside settlement area has a cluster and linear pattern that has an impact on the economic growth of the settlement and is not in harmony with the morphology of the riverbank with all its mixing activities. Furthermore, linear growth, where the growth of suburban settlements is only concentrated in areas that provide road access, and riverside land is still a water catchment area.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".