Developing a User Friendly Decision Tool for Agricultural Land Use Allocation at a Regional Scale
Bibliographic record
Abstract
Agricultural land use planning should always be guided by a reliable tool to ensure effective decision making in the allocation of land use and activities. The primary aim of this study is to develop a user friendly system on a spatial basis for agricultural land suitability evaluation of four groups of agriculture commodities, including food crops, horticultural crops, perennial (plantation) crops, grazing, and tambak (fish ponds) to guide land use planning. The procedure used is as follows: (i) conducting soil survey based on generated land mapping units; (ii) developing soil database in GIS; and (iii) designing a user friendly system. The data bases of the study were derived from satellite imagery, digital topographic map, soil characteristics at reconnaissance scale, as well as climate data. Land suitability evaluation in this study uses the FAO method. The study produces a spatial based decision support tool called SUFIG-Wilkom that can give decision makers sets of information interactively for land use allocation purposes.This user friendly system is also amenable to various operations in a vector GIS, so that the system may accommodate possible additional assessment of other land use types.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".