Optimization of irregular-grid cellular automata and application in risk management of wind damage in forest planning
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
This study demonstrated how cellular automata, using irregular grids, can be used to minimize the risk of wind damage in forest management planning. The development of a forest in central Finland was simulated for a 30-year period with three subplanning periods. A forest growth and yield model in association with a mechanistic wind damage model was applied to simulate forest growth and to calculate the length of stand edges at risk. Irregular cellular automata were utilized to optimize the harvest schedules for reducing the risk and maintaining a sustainable harvest level. The cellular automata produced rational results, i.e., new clearcuts were often placed next to open gaps, thereby, reducing the amount of vulnerable stand edges. The algorithms of the cellular automata rapidly converged and optimized the harvest schedules in an efficient way, especially when risk minimization was the only objective. In a planning problem that included even-flow timber harvesting objectives (harvest level equal to the total timber growth), the targets were almost achieved. Although the cellular automaton had slightly larger deviations of harvesting from the targets compared with other tested heuristic approaches (simulated annealing, tabu search, and genetic algorithms), it had the best performance when minimizing the expected wind damage.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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