Arahan Konservasi Mata Air Untuk Kebutuhan Air Bersih di Dusun Kediwung, Kalurahan Mangunan, Kapanewon Dlingo, Kabupaten Bantul, DIY
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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 it