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.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".