Utilization of a Web-Based Geographic Information System for Land Mapping and Some Its Overview: A Case Study in Sukabumi District, Indonesia
Bibliographic record
Abstract
Land used for residence and agriculture is a source of life, especially in Indonesia. This is because most of the sources of livelihood, especially those living at the sub-district level, are mostly engaged in agricultural activities. Therefore, land data collection is very important, starting at the sub-district level. Unfortunately, the information system that supports this does not yet exist, especially in the Sukabumi district. Hence, this study to overview and use a web-based Geographic Information System (GIS) for land mapping, especially in Sukabumi District, Indonesia. The method used in this study was to conduct interviews with land officials at the sub-district level and several communities in the area. Information system problems from potential users are then addressed by designing a web-based information system and integrating it with GIS applications. The developed information system provides menus and sub-menus adapted to the needs of potential users. So far, the information system has been accessible and can help land officials at the sub-district level and several communities in the area. This will provide the level of detailed information that local communities need in responding to information related to their lands to minimize social conflicts.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".