<b>Analisis Kemampuan Lahan di Kabupaten Bangka</b>
Bibliographic record
Abstract
This study aims to analyze land capability in Bangka Regency based on nine Land Capability Units (LCU) using a weighted scoring and overlay method. The analysis incorporates spatial data processed through Geographic Information System (GIS) applications such as QGIS. The classification divides the land into five zones: very low, low, moderate, high, and very high capability. The results show that Zone V (very high capability) dominates most of the region, especially in districts like Mendo Barat and Belinyu, indicating strong potential for land development. Conversely, Zone I (very low capability) covers only a minimal area, highlighting its unsuitability for intensive use. Factors influencing land capability include morphology, slope stability, drainage, erosion risk, water availability, and natural disaster vulnerability. These findings provide a spatial basis for sustainable land use planning and are expected to support local government policies in optimizing land utilization while minimizing environmental risks.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".