Cluster Analysis on Forest Health Conditions in Lampung Province
Bibliographic record
Abstract
One of the indicators in achieving the goal of sustainable forest management is maintaining forest health. Forest health can describe the good and bad conditions of forest management. Management is carried out based on the functions owned by the forest. With these different managements, there is a need to assess and map the current state of forest health across various parts. This study aimed to obtain values of forest health status in each plot for different forest functions and generate a cluster map of forest health status in other forest functions. This study was on three types of forest based on their functions: conservation forest, production forest, and protection in Lampung Province. The method used is the Forest Health Monitoring (FHM). Method to determine the health of forests using indicators of vitality, productivity, and biodiversity and using Web-GIS to create a map of the distribution of cluster plots. The sample plot used is in the form of cluster plots, with the number of each forest function is divided into 3 clusters whose status is categorized as good, moderate, and bad. Based on the research, it was found that the protected forest cluster 1 had bad health status, cluster 2 was good, and cluster 3 was moderate. The overall health condition of the production function is bad, and the forest health status of the conservation forest function is all good. The current distribution map of the forest's sanitary conditions for the three localities helps guide management decisions to be made soon. The conclusion obtained from this study is that existing forest functions influence forest health status because forest management is adjusted to forest functions so that each function has a different status of forest health conditions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".