Evaluating Land Carrying Capacity in Mount Bromo’s Special Purpose Forest Areas
Bibliographic record
Abstract
Most people depend on the forest for their livelihood.Therefore, it is essential to study to what extent the capability of the land in forest areas designated for special purposes on Mount Bromo can satisfy the food needs of the surrounding community.The purpose of this study was to understand and analyze the carrying capacity of the land in the specially designated forest areas of Mount Bromo.This research is exploratory and descriptive, conducted through a survey method that included interviews, focus group discussions, and questionnaires.Data were collected by obtaining secondary data from the Central Statistics Agency of Karanganyar Regency.The data were analyzed using quantitative descriptive methods.The land carrying capacity was calculated by determining the supply and demand for land to ascertain the land's carrying capacity.The results showed that the land supply from the availability of land in the specially designated forest areas of Mount Bromo was 4,599.22 hectares, and the land needs were 14.85 hectares; therefore, the carrying capacity of the land was in a surplus or sufficient condition.As part of long-term planning, the policy for developing the agricultural sector should be directed towards improving accessibility for marketing agricultural products beyond the region.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".