Analisis Indeks Kinerja Sistem Irigasi Daerah Irigasi Sambeng Kecamatan Kasiman Kabupaten Bojonegoro
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
Penelitian ini bertujuan untuk menganalisis indeks kinerja sistem irigasi di Daerah Irigasi (DI) Sambeng, Kecamatan Kasiman, Kabupaten Bojonegoro, dengan menggunakan pendekatan Penilaian Aset dan Kinerja Sistem Irigasi (PAKSI). Data dikumpulkan melalui observasi lapangan, wawancara dengan petugas dan petani, serta dokumentasi teknis jaringan irigasi. Komponen yang dievaluasi meliputi prasarana fisik, produktivitas tanam, sarana penunjang, organisasi personalia, dokumentasi, dan peran organisasi petani pengguna air (P3A). Hasil analisis menunjukkan bahwa rata-rata indeks kinerja sistem irigasi sebesar 32,84%, yang termasuk dalam kategori “Kurang dan Perlu Perhatian”. Faktor utama penyebab rendahnya kinerja adalah kerusakan pada jaringan irigasi dan rendahnya partisipasi P3A/GP3A/IP3A dalam pengelolaan air. Berdasarkan hasil ini, direkomendasikan untuk segera melakukan perbaikan infrastruktur, meningkatkan sistem inventarisasi aset, memperkuat koordinasi antarinstansi, serta mengadakan pelatihan peningkatan kapasitas pengelola irigasi. Diharapkan langkah-langkah tersebut dapat memperbaiki sistem distribusi air dan mendukung ketahanan pangan secara berkelanjutan.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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