Teknik Konservasi Mata Air Berdasarkan Karakteristik Di Kapanewon Samigaluh, Kabupaten Kulon Progo, Daerah Istimewa Yogyakarta
Bibliographic record
Abstract
Kalurahan Sidoharjo memiliki tiga mata air yang berperan dalam pemenuhan kebutuhan air bersih delapan dusun. Ketiga mata air tersebut yaitu Tuk Mudal, Cung Lanang, dan Slilin. Terdapat permasalahan utama pada mata air yaitu penurunan kuantitas, penurunan kualitas, serta tidak ada bak penampung atau bangunan pelindung. Tujuan dari penelitian adalah menentukan arahan konservasi teknik dan non-teknik yang ditentukan dengan karakteristik mata air. Penelitian ini menggunakan metode kombinasi dari kuantitatif dan kualitatif, metode pengumpulan data mencakup survei pemetaan, wawancara, pengukuran, uji laboratorium, metode sampling dengan purposive sampling, dan metode analisis mencakup matematis, skoring, analisis wawancara, evaluasi. Berdasarkan penelitian didapatkan ketiga mata air berkarakteristik rekahan; kontinuitasnya perennial spring; dengan debit masing-masing Tuk Mudal kelas V, Cung Lanang kelas VI, dan Slilin kelas VII; kualitas cukup baik dengan beberapa parameter masih melampaui baku mutu pada Tuk Mudal (TSS, COD, BOD, total-coliform), Cung Lanang (TSS, BOD, total-coliform), Slilin (DO, TSS, COD, BOD, total-coliform). Rencana konservasi teknis daerah imbuhan khususnya penggunaan lahan kebun dan semak belukar yaitu teras individu, sedangkan rencana konservasi teknis mata air yaitu pembangunan bak pelindung dan bak penampung sedangkan konservasi non-teknis yaitu pendekatan dengan sosialisasi kepada masyarakat dan instansi terkait.Kata Kunci: Mata Air, Karakteristik Mata Air, Potensi Mata Air, Konservasi Daerah Imbuhan, Konservasi Mata Air
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".