A Voronoi tessellation based approach to generate hypothetical forest landscapes
Bibliographic record
Abstract
Optimization models used for forest planning can be computationally complex and the demand for real forest data to test them far exceeds the supply. As a result, hypothetical forest landscapes are often used, although their capacity to match the characteristics of real forests is limited and they offer little control over important landscape metrics such as average adjacency. Using four landscape metrics that are believed to be relevant to the computational efficiency of forest harvest scheduling models, we describe a new method for generating hypothetical landscapes of prespecified characterization. The new approach produces landscapes based on Voronoi tessellation, created from points chosen by a combination of random point processes. Through a series of multiple regressions, the proposed algorithm determines appropriate control parameters to ensure that the output landscape will match target characteristics within a given statistical tolerance and with a predefined probability. The new method can produce landscapes with a wide range of specifications, covering the characteristics of real forests and extending into extreme cases unlikely to be encountered in reality. At the same time, the method provides greater flexibility and control over the generated landscapes than previous methods.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 0.001 |
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; both teacher heads agree on what is shown here.
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".