Complexity in long-term stand dynamics of mixed-species, multi-cohort stands using an imputation/copula tree growth model
Bibliographic record
Abstract
Long-term stand structural dynamics are complex due to stochastic processes within natural systems. Although forest growth and yield models are widely used to forecast stand dynamics, there are still limitations in their ability to capture the full range of outcomes. An individual tree imputation/copula (I/C) model using nearest neighbor imputation and copula sampling is used to generate multiple projections to estimate uncertainty of future stand structures. The Nova Scotia permanent sample plots (NSPSP; n = 3250) were used as the reference data to simulate 500-year projections of Acadian Forest stand development. Species composition was more uncertain than size structure. Initial levels of red maple ( Acer rubrum L.) basal area significantly impacted long-term forest stand dynamics. Red maple basal area generally increased in all stand types while balsam fir (BF; Abies balsamea (L.) Mill) and red spruce (RS; Picea rubens Sarg.) generally decreased. Despite the relatively simple structure of the I/C model, complex stand dynamics can be predicted; however, the model is limited by the range of conditions represented in the reference data. The ability to estimate uncertainty in long-term stand development and the potential to assess forest management planning risk makes the I/C modelling approach a potentially powerful tool. • Imputation/copula (I/C) model is a new approach to assess projection uncertainty. • I/C model reflects range of conditions represented in the reference data. • Species composition has more uncertainty than size structure. • Red maple amounts drive future forest structures. • Broader ranges of reference data will improve I/C model performance.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| 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) | 0.002 | 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".