Forecasting the partial cutting cycle for Québec yellow birch-conifer mixedwood stands
Bibliographic record
Abstract
Since the mid 1990s, partial cuts have been widely used in yellow birch–conifer stands (BJR, bétulaies jaunes résineuses) in the temperate forests of Québec. We studied the impact of residual basal area on stand composition and on the time required to reconstitute enough merchantable basal area to allow for a second partial cut, according to the usual standards of forest management in Québec. To do so, we used a dataset from 9 experiments as well as simulations of the Artémis-2014 growth model and those of a new model, BJR, which we calibrated using the study data. Our results show that residual basal area influences stand periodic annual increment, which peaks 10 to 15 years after the cut. Residual basal area also influences the length of the cutting cycle and future stand composition. We estimated a mean cutting cycle of 24 years for a mean residual basal area of 18 m2·ha-1, and of 40 years for a mean residual basal area of 14 m2·ha-1. For the latter, our results also show that some opportunistic species of lesser commercial value, such as red maple, could become more abundant.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".