Spatial simulation of forest succession and timber harvesting using LANDIS
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
The LANDIS model simulates ecological dynamics, including forest succession, disturbance, seed dispersal and establishment, fire and wind disturbance, and their interactions. We describe the addition to LANDIS of capabilities to simulate forest vegetation management, including harvest. Stands (groups of cells) are prioritized for harvest using one of four ranking algorithms that use criteria related to forest management objectives. Cells within a selected stand are harvested according to the species and age cohort removal rules specified in a prescription. These flexible removal rules allow simulation of a wide range of prescriptions such as prescribed burning, thinning, single-tree selection, and clear-cutting. We present a case study of the application of LANDIS to a managed watershed in the Missouri (U.S.A.) Ozark Mountains to illustrate the utility of this approach to simulate succession as a response to forest management and other disturbance. The different cutting practices produced differences in species and size-class composition, average patch sizes (for patches defined by forest type or by size class), and amount of forest edge across the landscape. The capabilities of LANDIS provide a modeling tool to investigate questions of how timber management changes forest composition and spatial pattern, providing insight into ecological response to forest 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 | 0.001 | 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 | 0.000 | 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