Optimal allocation model of forest fire detection towers in protected areas based on fire occurrence risk: Where and how to act?
Bibliographic record
Abstract
Forest fire detection towers are crucial in supporting rapid firefighting actions in conservation units and thus reducing environmental, social, and economic damages. Thus, the aim was to evaluate scenarios for optimal allocation of forest fire detection towers, according to the risk of occurrence, in the Caparaó National Park, Brazil. Thus, by geotechnological analysis, the areas most susceptible to forest fires and the optimal locations for installation of detection and monitoring for these events were delimited. To run the proposed models, biological, physical, socioeconomic, and meteorological variables were used. From the application of the methodologies, it was observed that 76.70% of the study area was covered by low, moderate, and shallow fire risk classes, while high and very high-risk classes were concentrated in the buffer zone. The scenario with 45 towers was considered the most advantageous, given that they presented viewing levels above 70% and a lower cost per hectare viewed than the scenario with 48 towers. Results showed no critical risks of fire occurrence within the conservation unit, but preventive measures are still needed to avoid fire spread, particularly near the buffer zone. The study's methodologies can be applied in other areas to improve forest fire prevention and control efforts.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".