Mapping and Analysis Factors of Affecting Productivity Tropical Rain Forests in East Kalimantan
Bibliographic record
Abstract
Up to 2019, tropical rainforests in East Kalimantan has been experiencing very rapid degradation and continues to shrink. Therefore, it is necessary to evaluate mapping and analysis of factors affecting the productivity of tropical rain forests in East Kalimantan. The purpose of this study was to determine the factors that cause shrinkage of tropical rainforests in East Kalimantan based on spatial statistical perspectives. The data used were secondary data from the Indonesian Ministry of Forestry and the Central Bureau of Statistics. The data consisted of 10 districts/cities from East Kalimantan Province. Those data were influenced by spatial dependence and spatial heterogeneity. Nonparametric Geospatial Regression (NGR) is one of the spatial statistical methods used to overcome spatial dependence and spatial heterogeneity. The results of the study obtained was a Nonparametric Geospatial Regression modeling using the Gaussian Kernel geographical weighting function with a minimum CV value of 1.48. The model had R2 values for each district/city ranging from 74.39% - 88.65%. The goodness of fit of the NGR model was shown by the value of R2 = 0.8865, which stated that the variables that significantly affect the preservation of tropical rainforest by 88.65% were the area of protected forests, nature reserves and nature preservation, production forests, area of each district/city, economic growth rate and regional development index.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".