DELINEASI POTENSI BENTONIT MENGGUNAKAN METODA GEOLISTRIK DI DESA SEKARWANGI, KECAMATAN CURUGBITUNG, KABUPATEN LEBAK, PROVINSI BANTEN
Bibliographic record
Abstract
Bentonite is an abundant mineral resource in Indonesia, based on data from the Center for Coal and Geothermal Mineral Resources (PSDMBP) in 2021 the measured resource of bentonite is 3,299,072,992.75 tons and has not been utilized optimally. Sekarwangi Village has bentonite resources, based on that the purpose of this research was conducted to find out the potential and types of bentonite found in the Sekarwangi area so that it can be put to good use. Sekarwangi Village is located in Curugbitung District, Lebak Regency, Banten Province, Indonesia. This research is divided into two stages, namely field activities and laboratory activities. Field activities include geological observations and geoelectric measurements. Laboratory activities are testing the geochemical content of rocks in the form of XRF (X-ray Fluorescence) and XRD (X-Ray Diffraction)analysis. The result shows that the rocks are composed of volcanic deposits, one of which is tuff. Based on the results of chemical analysis, bentonite in the study area is classified in the Ca-Bentonite category. Potential bentonite layer can be found at the depth of 1.27 - 40.24m. Bentonite has thickness ranging from 9.6m to 34.61m. The resistivity value ranges from 0.09 - 9.69 ohm/m.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".