Predicting the effect of thinning on growth of dense balsam fir stands using a process-based tree growth model
Bibliographic record
Abstract
A tree-level process-based model of forest growth is used to investigate the effects of thinning on the growth of balsam fir (Abies balsamea (L.) Mill.) in stands that have almost reached commercial maturity but that have never been thinned. The model is applied to predict the 20-year growth of a stand following a recently established thinning experiment in which four thinning treatments were tested. The combination of stand properties and treatment type is quite particular and the resulting long-term effect on growth cannot be evaluated based on past experiments. The objectives of the study are to provide estimates of treatment outcome and of their errors over the appropriate time frame for decision making. This is achieved by representing growth processes through functions empirically adjusted to field observations while limiting the inputs of the model to what are usually available through regular forest inventory. Simulations suggest that 20-year growth of individual trees from the smaller diameter classes is improved by the treatments, but the growth of larger trees (>0.1 m3) is left unchanged. When the model error is not taken into account, the results after 20 years suggest, with a confidence level greater than 95%, that the merchantable volume of the treated plots does not recover to the level found in the untreated control plots, a result contrary to the initially expected effect of such thinning. By including modelling uncertainty, however, the confidence level associated with such a result is reduced to 70%. Such an inclusion prevents the misuse of the model predictions too far into the future.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".