ANALISIS PRIORITAS IMPLEMENTASI MODERNISASI IRIGASI PADA DAERAH IRIGASI WAY SEKAMPUNG
Bibliographic record
Abstract
Sistem irigasi di Indonesia mulai berkembang pada dekade awal 1970-an. Dengan kondisi lingkungan saat ini yang mengalami perubahan, baik strategis maupun ekologi, berdampak pada perubahan sistem irigasi yang dapat menyebabkan pengelolaan air irigasi menjadi semakin buruk. Modernisasi irigasi muncul sebagai solusi strategis untuk meningkatkan efisiensi, ketahanan, dan keberlanjutan sistem irigasi dalam rangka mendukung ketahanan pangan dan air. Daerah Irigasi Way Sekampung terdiri dari tujuh sub daerah irigasi dengan luas layanan mencapai 55.000 ha, yaitu Sub DI Bekri, Sub DI Sekampung Batanghari, Sub DI Bunut, Sub DI Batanghari Utara, Sub DI Raman Utara, Sub DI Punggur Utara dan Sub DI Rumbia. Implementasi modernisasi irigasi Daerah Irigasi Way Sekampung dinilai berdasarkan indeks kinerja sistem irigasi dan indeks kinerja modernisasi irigasi. Dengan analisis SWOT, dapat diperoleh prioritas implementasi modernisasi Daerah Irigasi Way Sekampung. Hasil analisis diharapkan dapat menjadi masukan kepada pemangku kebijakan untuk penerapan modernisasi irigasi di Daerah Irigasi Way Sekampung, termasuk manfaat, tantangan, serta implementasi di lapangan.
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.
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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".