Pengelolaan Mata Air Karst Sebagai Sumber Air Domestik Di Dusun Duwet, Desa Purwodadi, Kecamatan Tepus, Gunung Kidul, D.I. Yogyakarta
Bibliographic record
Abstract
ABSTRAKAir merupakan salah satu kebutuhan pokok bagi kehidupan manusia. Dusun Duwet, Desa Purwodadi termasuk kawasan bentang alam karst yang memiliki tingkat kelangkaan air tinggi. Pada daerah tersebut terdapat tiga mata air yang mengalir sepanjang tahun, namun pada musim kemarau debit mata air mengalami penurunan kuantitas. Tujuan penelitian ini yaitu menyusun cara pengelolaan mata air pada daerah karst untuk digunakan sebagai sumber air domestik. Metode penelitian yang digunakan yaitu survei dan pemetaan lapangan, matematis dengan menghitung debit mata air dan volume bak penampung, evaluasi, dan wawancara. Karakteristik mata air yang dikaji meliputi sebaran dan tipe mata air berdasarkan debit. Potensi mata air diketahui dari kuantitas dan kualitas air. Hasil penelitian menunjukkan bahwa ketiga mata air termasuk tipe perlapisan/kontak dengan sifat pengaliran menahun (perenial springs). Berdasarkan kelas debit mata air Kaliwonosari dan Kaliduren termasuk kelas sedang, sedangkan Luweng Nglibeng termasuk kelas tinggi. Secara umum kualitas air pada ketiga mata air baik untuk digunakan keperluan domestik sehari-hari. Pengelolaan mata air dilakukan secara teknik dengan pembuatan teras bangku dan sarana Perlindungan Mata Air (PMA) dengan pendekatan berbasis masyarakat dan pemerintah. Kata kunci: mata air, karst, karakteristik, potensi, konservasi
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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 | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 0.008 |
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".