Stand dynamics modelling approaches for multicohort management of eastern Canadian boreal forests
Bibliographic record
Abstract
The objective of this paper is to discuss approaches and issues related to modelling stand dynamics for multi-cohort forest management in eastern Canadian boreal forests. In these forests, the interval between wildfires can be rather long, and the development of natural forest stands may include the establishment, growth and mortality of several cohorts of trees. Later cohorts are characterised by increasing structural complexity, including spatial heterogeneity and irregular tree size distribution. A multi-cohort forest management framework has been proposed to maintain this complexity, and associated biodiversity, on the landscape. Multi-cohort forest management planning requires forecasts of the development of stands with complex structure in response to silvicultural treatment and to natural disturbance, but current stand dynamics models in the region are applicable mainly to even-aged mono-specific stands. Possible modelling approaches for complex stands include i) the adaptation of current whole-stand growth and yield models, ii) distance-independent, empirically-derived individual-tree models, such as the USDA Forest Service Forest Vegetation Simulator, and iii) distance-dependent, empirically-derived or process-oriented individual-tree models. We conclude that individual-tree models are needed because observational data for fitting whole-stand models are not available for the full array of silvicultural treatments and natural disturbances encompassed by multi-cohort forest management. Predictive accuracy is a concern with individual-tree models, and the incorporation of coarse-scale constraints into these models is a promising means to control error.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".