Fuzzy logic applied in the prospecting of suitable areas for the establishment of commercial forest plantations
Bibliographic record
Abstract
Finding suitable areas for the establishment of commercial forest plantations is a crucial step towards the technical and financial viability of forestry enterprises. Thus, this study aimed to propose an alternative methodology to define areas with greater potential for the establishment of commercial forest plantations through the application of fuzzy logic. Edaphoclimatic zoning of the main forest genetic materials used in the state, land use classes, road networks, terrain slopes, and environmental regulation of rural properties was included in the modeling. In addition, a network analysis was applied to delimit the optimal transport radius. The results referring to the optimal areas for forest planting, according to the model, obtained an average of 80.39% assertiveness in the validation test in relation to areas already consolidated with forest plantations in the study area, demonstrating the potential of fuzzy logic to find areas favorable for future plantations, with low cost involved in prospecting areas. Therefore, it is concluded that the proposed methodology has high accuracy and low processing cost and can be used to improve planning. Its application can be expanded to other Brazilian regions as well as other countries.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".