Arahan Konservasi Mata Air Untuk Kebutuhan Air Bersih di Dusun Kediwung, Kalurahan Mangunan, Kapanewon Dlingo, Kabupaten Bantul, DIY
Bibliographic record
Abstract
Dusun Kediwung memiliki tiga mata air yaitu Mata Air Pancuran, Mata Air Kediwung, dan Mata Air Gumelem sebagai sumber air utama digunakan untuk kegiatan sehari-hari. Selama musim kemarau, terdapat penurunan kuantitas pada ketiga mata air. Dusun Kediwung pernah mengalami kemarau panjang sehingga dibutuhkan konservasi mata air untuk memenuhi terhadap kebutuhan air bersih warga. Penelitian ini memiliki tujuan untuk mengetahui arahan konservasi yang tepat untuk mata air dan daerah imbuhan. Metode penelitian menggunakan metode survey dan pemetaan, metode volumetrik, wawancara, pengolahan data kuantitatif, uji laboratorium, serta metode sampling dengan purposive sampling. Secara debit Mata Air Pancuran dan Mata Air Kediwung kelas VI sedangkan Mata Air Gumelem kelas VIII. Mata air hanya memenuhi kebutuhan air bersih sebanyak 35.000 L/hari, namun masih kekurangan air bersih sebanyak 11.900 L/hari. Mata air di Dusun Kediwung mengandung kesadahan yang dapat membahayakan kesehatan. Arahan pengelolaan yang digunakan ialah dengan pembuatan bangunan Pemanenan Air Hujan (PAH) dan bangunan filtrasi dalam memenuhi kebutuhan air bersih di Dusun Kediwung.Kata kunci: Mata Air, Penangkap Air Hujan, Filtrasi, Sanitasi Air, SDG
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.007 |
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".