Kajian Optimalisasi Sistem Irigasi Rawa (Studi Kasus Daerah Rawa Semangga Kabupaten Merauke Propinsi Papua)
Bibliographic record
Abstract
Population growth is increasing, but it is not accompanied by an increase in food needs impartial. Indonesian swamp land potential of about 33.4 million ha, consisting of tidal swamp 20.1 million ha and 13.3 million ha of lowland swamp. The Government has made the development of swamps into agricultural land, including the Semangga swamp area (4,000 ha) The cropping pattern of rice (100%) - crops (40%) – “bero”. The problem faced are; the length is 7-month of dry season and low agricultural production, are therefore likely to swamp irrigation system optimalization. The method used to carry out water balance analysis and performance assessment of irrigation system include; the physical condition of irrigation, the application of the system of planting and water delivery techniques to the use of land for a year, then performed according to the potential land development plan and water resources available. Results of water balance analysis on Semangga Swamp Area existing condition indicate that water deficit occurred during the second growing season crops (May-July) and in December. So do the appropriate development plan defined cropping pattern III, namely rice (100%) - crops (60%) - crops (45%) with the addition of a total area of 1,000 ha through the use Kumbe River and Maro River and other water reservoirs to overcome deficits in the availability of water in the dry season that is equal to 2.5m³/s (April to August and October to December), while 6.5m³/s in September, 72.40% irrigation system performance with good category.
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 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".