PEMODELAN PERUBAHAN PENGGUNAAN LAHAN DI KABUPATEN KAMPAR
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study aims to analyze the drivers of land use change in Kampar District and to make modeling of land use changes in 2028. This study uses a survey method. To find out the factors driving the changes in land use in Kampar District were analyzed using binary logistic regression with a stepwise method. Forward land use prediction in 2028 with 3 scenarios was carried out by modeling using Markov Chain and Cellular Automata (CA). The results showed that the density population, altitude, slope, distance to the main road, distance to the river, and distance to the subdistrict city are the driving factors that influence changes in the use of forest land to plantations, forests to open land, mixed plantations to built up land, and mixed plantations to plantations in Kampar District The results of land use modeling in 2028 using CA-Markov with 3 scenarios indicate an increase or reduction in several types of land use, especially forests, plantations, paddy fields and built up land use, forest land use decreases widely in scenario I, and scenario II , on the contrary experience an increase in area in scenario III; plantation land use shows the addition of the three scenarios created; Likewise the developed land shows addition to all three scenarios; Furthermore, there is a reduction in paddy fields in scenario I and scenario II, but in scenario III the use of paddy fields does not experience any addition or reduction
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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 | 0.001 |
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 it