Hydrological and Soil Characteristics of Long Storage Systems for Food Security in Merauke, Indonesia
Bibliographic record
Abstract
Long storage is an important infrastructure for irrigation water storage in Merauke, but its utilization is not optimal because the water source depends on rainwater, and causes a serious problem.Therefore, it is necessary to know the surface water runoff amount to understand the flood discharge received by long storage.This study aims to determine the value of runoff coefficient and soil characteristics in long storage to determine the effectiveness of long storage in supporting national food security.This research also represents the first investigation of runoff coefficients in Indonesian swamp areas.The study uses soil tests and hydrological analysis.Soil tests through field and lab work identify properties affecting water retention.Hydrological analysis includes calculating discharge and runoff coefficients to evaluate long storage performance.The test results show that the soil around the long storage is low plasticity silt with very low permeability (0.0000544-0.0000744 cm/sec).The runoff coefficient values varied between 0.0969 to 0.7103 each month.Based on the results, we conclude that long storage is suitable for a long-term irrigation water reservoir due to its impermeable soil characteristics, but more efficient integration of groundwater recharge and irrigation systems is needed for its sustainable use.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".