Pendugaan potensi sumber air tanah menggunakan metode geolistrik konfigurasi Schlumberger di desa Srowot kecamatan Kalibagor kabupaten Banyumas
Bibliographic record
Abstract
Survei geolistrik resistivitas dengan konfigurasi Schlumberger telah dilakukan untuk menduga kedalaman lapisan akuifer air tanah di Desa Srowot Kecamatan Kalibagor Kabupaten Banyumas. Akuisisi data dilakukan di enam titik sounding, yaitu titik SR-1, SR-2, SR-3, SR-4, SR-5, dan SR-6 dengan panjang bentangan 200 m. Hasil survei menunjukkan bahwa lapisan akuifer di daerah penelitian terdiri atas akuifer tertekan, akuifer bebas, dan akuifer semi tertekan. Akuifer tertekan terdapat di titik SR-1 berupa pasir berbutir halus (2,36 Ωm) pada kedalaman lebih dari 19,58 m. Adapun akuifer bebas dan/atau akuifer semi tertekan terdapat pada titik SR-2, SR-3, SR-4, SR-5, dan SR-6. Pada titik SR-2, lapisan akuifer berupa lempung pasiran (13,90 Ωm) dengan kedalaman 10,62-22,61 m. Pada titik SR-3, lapisan akuifer berupa lempung pasiran agak mampat (56,80 Ωm) dan lempung pasiran (15,70 Ωm) pada kedalaman 10,04-22,44 m dan lebih dari 50,04 m. Pada titik SR-4, lapsian akuifer berupa pasir berbutir sedang (6,91 Ωm) pada kedalaman lebih dari 64,40 m. Pada titik SR-5, lapisan akuifer berupa pasir lempungan (4,79 Ωm) pada kedalaman 2,74−25,04 m dan pasir berbutir halus (2,82 Ωm) pada kedalaman lebih dari 46,64 m. Sedangkan pada titik SR-6, lapisan akuifer berupa pasir lempungan (19,00 Ωm) pada kedalaman 9,29−22,60 m serta pasir berbutir halus (2,70 Ωm) pada kedalaman lebih dari 46,60 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".