Simulating stand-level harvest prescriptions across landscapes: LANDIS PRO harvest module design
Bibliographic record
Abstract
Forest landscape models (FLMs) are an important tool for assessing the long-term cumulative effects of harvest over large spatial extents. However, they have not been commonly used to guide forest management planning and on-the-ground operations. This is largely because FLMs track relatively simplistic vegetation information such as age cohort presence/absence, forest type, and biomass that are incompatible with tree density and size on which most harvest prescriptions are based. We describe and demonstrate the newly developed harvest module of the LANDIS PRO FLM, which tracks density, size, basal area, and stocking by species age cohorts for each site (cell). With this quantitative information, the module can simulate basal area controlled harvest, stocking-level controlled harvest, and group selection harvest. Through user-specified harvest year (frequency), stand ranking, and species and age preference, the new module can simulate a wide variety of harvest prescriptions such as thinning from above and below, shelterwood, clear-cutting, and group selection. We applied the LANDIS PRO harvest module to a large (17 000 km2) central hardwood forest landscape in Missouri. The simulated harvest prescriptions produced realistic stand-scale results when plotted on Gingrich stocking charts. The harvest module improves on previous versions by allowing partial treatment of individual age-classes within a cell and reporting results in metrics commonly used in stand-scale silviculture. It provides a closer link between landscape-scale simulation methods and stand-scale management.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".