Pemodelan Spasial Perubahan Penggunaan Lahan di Taman Nasional Gunung Halimun Salak dan Daerah Penyangganya
Bibliographic record
Abstract
Land use activities in Gunung Halimun Salak National Park (GHSNP) that does not comply with the zoning plan of GHSNP cause degradation, deforestation and decreasing GHSNP size, while land use activities intensively in the surrounding of GHSNP (buffer area) that does not comply with the spatial allocation plan may alter landscape configuration that influence ecological processes and biodiversity within national park. Predicting land use and land cover (LULC) change patterns in the future provides important information for identifying areas that vulnerable to changes. Multi-temporal remote sensing data was used to identify LULC, a multi-layer perceptron neural network with a Markov chain model (MLPNN-M) was used to predict LULC in 2025 and to analyze LULC trend, Overlaying analysis was used to analyze the consistency between LULC and spatial allocation regulation in 2025. The results show that LULC in GHSNP and its buffer area consist of prmary forests, secondary forests, mixture crops, plantations, settlements, agriculture, shrubs, and water. The primary forests, secondary forests, mixture crops, and agriculture were predicted to decrease while settlements, plantations and shrubs were predicted to increase. Land conversion trends into secondary forests, plantations, agriculture and shrubs that begin to show centralized patterns within and the boundaries of GHSNP need to be anticipated. In 2025, inconsistency between land use and GHSNP zonation is the existence of mixture crops, plantations, settlements and agriculture outside the special zone whereas inconsistency between land use and spatial allocation regulation is existence of plantations and agriculture in conservation forest, protection forest and production forest.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".