Potensi Pasang Surut Lahan Rawa untuk Pengembangan Irigasi di Kabupaten Merauke Menggunakan Pemodelan Hidrodinamika 1D2D
Bibliographic record
Abstract
Merauke Regency has three major rivers i.e Bian River, Kumbe River and Maro River (BIKUMA), the three rivers have large horizontal tidal potential. To know the potential of tides in the development of lowland irrigation in Merauke Regency needs to be studied. This study is supported by hydrometry and hydraulic surveys which has been conducted during the dry season during at spring tide and neap tide simultaneously for all three rivers. The survey included measuring river geometry activities with a range of 5 Km, river hydrometry measurements (observation of water fluctuations with proportional distances for model calibration and upstream river velocity for discharge). Limitations of river upstream measurements are limited by the distance where the Bian River along 125 Km, the River Kumbe along 171 km, and the Maro River along 66 km from the estuary. Then, performed a Sobek 1D hydrodynamic modeling that describes the movement of water from upstream into downstream. From the results of modeling is known that the water entering from the sea to the Bikuma River is greater than the water out to sea. The potential for tides is 1.7 Billion m3. Furthermore, the simulation of Sobek 1D2D to obtain the extent of natural condition, the area that can be inundated is 123.609 ha. Utilization of tidal potential can be channeled to the development zone through an integrated lowland irrigation water management system so that water utilization can be optimal.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.020 | 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".