Determination of the Agricultural Land Potential Index Using a Geographic Information System: A Case Study of Aceh Tengah Regency, Indonesia
Bibliographic record
Abstract
Agricultural problems that often arise are due to a lack of suitable and strategic agricultural land for its use, which results in poor agricultural production in the area. A land potential index that classifies existing land potentials from high to low class can be used to overcome this. This study aims to build an agricultural land potential index using a geographic information system in the Regency of Aceh Tengah, Indonesia, using a geographic information system. The method used in this research is a survey approach to collect information in the form of rainfall data, slope, lithology, soil type, land use and administrative maps of the Aceh Tengah Regency. The land potential index is obtained by overlaying the slope parameters, lithology, soil type, hydrology, and susceptibility to erosion into a land map unit that can classify it into five classes of a land potential index. The results of this study indicate that the Regency of Aceh Tengah is included in the very wet climate type. Maximum erosion was 1,213.6 tons per ha per year. The land potential index with very low criteria was 23.38% (102,002.42 ha) with a slope greater than 40%. The land potential index with very high criteria has an area of 3,807.80 ha (0.87%) with a maximum slope of 15%. A land potential index with very high criteria was found in the Linge, Atu Lintang, Lut Tawar, Pegasing, Bintang, Jagong Jeget, Kebayakan, Ketol, and Celala districts with an area of 2,014.26 ha, 1,266.33 ha, 174.81 ha, 148.07 ha, 77.86 ha, 73.63 ha, 46.77 ha, 4.14 ha and 1.94 ha, respectively. Meanwhile, the land potential index with very low criteria is found in all districts except Kute Panang and Atu Lintang.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".