Dynamic simulation of land-use changes in a periurban agricultural system
Bibliographic record
Abstract
Studying the driving forces of land-use change dynamics (LUCD) is important for understanding the change process. A spatially explicit simulation model helps to test hypotheses about landscape evolution under several scenarios. This paper presents a dynamic simulation model of land-use change (LUC) of the Nong Chok area in Central Thailand. Simulation of LUCD has been performed integrating remote sensing, geographic information systems (GIS), and the dynamic simulation toolkit. The model is a cellular automata model that has been developed on the basis of selected spatial and human driving forces. This study was conceived for the simulation of LUC, in particular, from paddy fields to fishponds. The model was run for 19 years from 1981 to 2000. Data describing present and historic land-use patterns were derived from aerial photographs. Transition functions were developed using the ID3 algorithm of the LUC data sets. The model uses as its input a land-use map (1981) and spatial and human variables: distance to canal and age, ownership, religion, education, and family size of the farmers. The results of the simulation showed substantial ability of the model to diffuse fishponds. To validate this spatial simulation model of LUCD, the simulated maps were compared with the reference land-use maps using a set of landscape indices: number of fishpond cells, patch density, mean patch size, edge density, fractal dimension, and mean nearest neighborhood.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".