Modelling the stand dynamics after a thinning induced partial mortality: A compensatory growth perspective
Bibliographic record
Abstract
Introduction: With increasing forest areas under management, dynamics of managed stands have gained more attention by forest managers and practitioners. Improved understanding on how trees and forest stands would respond to different disturbances is required to predict the dynamics of managed stand.s. Partial mortality commonly occurs in stand development, and different response patterns of trees and stands to partial mortality would govern stand dynamics. Methods: To investigate the possible response patterns using existing knowledge of growth and yield relationships, we developed TreeCG model, standing for Tree's Compensatory Growth, a state-dependent individual tree-based forest growth model that simulates the compensatory growth of trees after experiencing a partial mortality. The mechanism behind the simulation is the redistribution of resources, including nutrients and space, freed from died trees to surviving trees. The developed new algorithm simplified the simulations of annual growth increments of individual trees over a long period of stand development. Results: The model was able to reproduce the forest growth patterns displayed in long-term precommercial thinning experiments. The simulated forest growth displayed the process of compensatory growth from under compensation, to compensation-induced-equality, and to overcompensation over time. Discussion: Our model can simulate stand growth trajectories after different partial harvest regimes at different times and intensities, thus support decisions in best partial harvest strategies. This generic model can be refined with given tree species and specific site conditions to predict stand dynamics after given partial mortality for any jurisdictions under management. The simulation reassembles growth trajectories of natural stands when no thinning is conducted.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".