Understanding Forest Fire Disaster Management in Indonesia with Global Perspective
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
Forest fire becomes one of the attentions of countries in the world. Some countries with the largest forest cover in the world such as Russia, Brazil, Canada, United Stated, and Indonesia have massive forest fire record. Thus, it is important to have forest fire management in order to decrease the level of forest fire. Current conditions indicate that Indonesia can significantly reduce forest fires within the past 3 years compared to those 4 countries. Therefore, it is necessary to study the characteristics of forest fire disaster management based on global perspective. The method used in this research is scoring for each parameter of disaster management with descriptive analysis. The results obtained show that Indonesia has an advantage in the field of legal regulation change in a short time so that the incidence of forest fire fell significantly compared with Russia, Brazil, Canada, United States. However, Indonesia still has weaknesses in emergency response, forest fire monitoring technology, and inter-institutional integrity in forest fire disaster management.
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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 | 0.001 | 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 it