Soil Moisture Monitoring in Banjarnegara Regency Using SMAP imagery
Bibliographic record
Abstract
Abstract One of the places in Indonesia where landslides occur relatively frequently, is Banjarnegara Regency. Landslides with significant losses are observed in the study area almost annually. The preceding soil wetness is one of the elements that trigger landslides. In this study, we determine high and low soil wetness by classifying soil moisture and observing how it relates between soil moisture and the occurrence of landslides. Temporary and spatial processing was done on historical soil moisture imaging data from the Soil Moisture Active Passive (SMAP) satellite. The soil moisture variability in each of the 20 grids in the study area was compared. The highest and lowest soil moisture distribution is noticeable using the high-frequency approach. The findings demonstrate that the historical soil water content trend often follows a similar pattern. When divided by the number of samples (n), the Very High-Frequency method has a minimum value of 0 and a maximum value of 25.4mm. In this value range, there are five different classes of soil moisture: very low (0–5mm), low (5.1–10mm), medium (10.1–15mm), high (15.1–20mm), and very high (20.1-25.4mm). Despite not being the highest value from very high-frequency computations, the Banjarnegara Regency is in the high class, ranging from 16.12mm to 20.03mm. The elements causing the frequent landslides in Banjarnegara Regency are excessive and antecedent soil moisture. From 894 historical data records, entire grids with very-high and high classes cumulatively have between 51% and 85% incidences of soil moisture value.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".