ANALISIS KAWASAN RAWAN LONGSOR DAN KETERKAITANNYA TERHADAP KUALITAS TANAH DAN PENGGUNAAN LAHAN (Kasus di Kawasan Agribisnis Juhut Kabupaten Pandeglang)
Bibliographic record
Abstract
AbstractJuhut region based on the government policy of Banten Province in 2015 is an area that will be developed as a center of agribusiness development area. Its existence is located on the slopes of the hills and at the same time as a residential area allegedly as an area prone to landslides. Agriculture cultivation activities related to soil fertility conditions that are on the belt of the volcano, making this area attracts the community to conduct agricultural cultivation activities despite being on a slope land. The aims of this study are the biophysical analysis of landslide hazard areas and their relationship to soil quality and land use patterns.The method used in this study is observation and groundceck of field biophysical condition and overlay analysis of thematic map related to landslide prone condition. The results of the study indicate that the biophysical condition of the land indicates that the landslide prone areas are susceptible to 707.1 Ha (70%), while the high vulnerability area reaches 245.3 Ha (24%). Soil fertility causes attractive soil to be managed by the community for the cultivation of seasonal crops or horticultural crops that can further trigger a landslide. This is also because the soil type conditions also have physical properties that are vulnerable to the early behavior of seasonal changes (from dry season to rainy season). Land use that is not suitability with morphological conditions of land and already managed by the community as an economic source will be a threat of high vulnerability to landslide hazards.Keywords : agribisnis area, land slides hazard, soil biophysical, land quality
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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".