KETERCUKUPAN AIR TEMPORAL SEBAGAI INDIKATOR KETERSEDIAAN AIR KAWASAN (STUDI KASUS DAS CILIWUNG HULU)
Bibliographic record
Abstract
DAS Ciliwung Hulu wilayahnya meliputi kawasan wisata puncak, menpunyai curah hujan rata-rata tahunan lebih besar dari 3.000 mm, namun beberapa kawasan mengalami kekurangan air baku diwaktu musim kemarau. Kondisi tersebut akibat pembangunan lahan untuk pariwisata atau pemukiman dengan laju 12.34% per tahun, sehingga air hujan yang masuk ke dalam tanah (infiltrasi) hanya 20%. Tujuan dari penelitian ini adalah untuk mengetahui ketersediaan air sepanjang tahun dari masing-masing sub DAS di DAS Ciliwung Hulu dengan indikator ketercukupan air temporal. Metode yang digunakan adalah: F.J. Mock untuk analisis debit andalan, Indeks Pollutan untuk analisis kualitas air, neraca air untuk surplus dan defisit air, dan indeks ketercukupan air temporal (IKaT). Hasil analisis menunjukkan: Sub DAS Ciseuseupan untuk katagori ketersediaan air termasuk ke dalam katagori tidak cukup, sedangkan dalam katagori ketercukupan air temporal masuk kurang cukup; Sub DAS Cibogo dalam katagori ketersediaan air masuk dalam status kurang cukup, namun dalam katagori ketercukupan air temporal termasuk ke dalam status sedang: sub DAS Cisarua baik untuk katagori ketersediaan maupun ketercukupan air temporal masuk dalam status sedang; dan sub DAS Ciesek, Ciliwung Hulu, dan Cisakabirus mempunyai skor 1 baik untuk ketercukupan air temporal maupun keterseiaan 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".