Identification of Aquifer Potential by Geoelectric Method in Gedangsari District, Gunungkidul Regency
Bibliographic record
Abstract
Water is a very important natural resource that is needed by all living things in all their life activities. Research related to water and how to use water resources is an important issue. The amount of fresh water available on earth is only about 25% of the total water. The aim of this study is to identify potential aquifers with the geoelectric method. This can be a consideration in determining the right location of the well in relation to efforts to obtain potential groundwater resources to meet the needs of raw water, both in terms of quantity and quality. The method used is a field survey by making observations including rock permeability measurements, infiltration, laboratory and studio work. Field work also includes geoelectric measurements, observations of groundwater levels and land use, and analysis of aquifers to determine the location of drill points that have the potential to be developed. The results show that the supply of raw water for a number of locations spread across Gunungkidul Regency is very much needed, considering that in the dry season there is a shortage of water. Groundwater conditions using the Schlumberger Method show that they have sufficient raw water potential, but in terms of depth and discharge, each location is different from one another. Planning for borehole construction is highly recommended, and planning is adjusted to the amount of demand and availability of raw water.
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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 | 0.002 | 0.001 |
| 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) | 0.001 | 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".