Resistivity Model of Clayshale Layers in Dry Season and Early Rainy Season Conditions Case Study of the Jragung Dam Project
Bibliographic record
Abstract
The use of geoelectricity as subsurface data acquisition will be very helpful if used to perform correlations on rock formations. The aim of this research is to look at the pattern of subsurface resistivity values compared to the condition of rock layers or outcrops in the field. The geoelectric survey used the dipole dipole method with the Multichannel Resistivity MAE X 612 EM+ instrument. The case study was carried out at one of the excavation locations at Jragung Dam with sandstone and clay stone lithology with varying thicknesses. Conditions in the field are that the clay stone layer is starting to experience greater deformation compared to the sandstone layer. Worse deformation in claystone in the field is caused by durability values which are generally worse than sandstone and the greater water content in claystone even though its compressive strength is relatively greater. In the dry season, sandstone (Reference point 1) at a depth of 5m has a resistivity of >86 ohm.m, while claystone has 12 - 15 ohm.m. At the beginning of the rainy season sandstone 37 – 50 ohm.m, clay stone (reference point 1) resistivity 8-11 ohm.m. The resistivity of claystone does not change significantly with changes in conditions. Because the porosity and permeability of sandstone can change significantly under changing
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 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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".