Sapling growth dynamics after partial cutting in temperate mixedwood stands
Bibliographic record
Abstract
The temperate mixedwood forests of eastern North America have been managed by partial cutting for several decades. To ensure that regeneration contributes to replacing the commercial-size stems that are removed by partial cutting, forest managers need to anticipate how saplings (i.e., regenerating trees with a diameter at breast height >1.0 cm) develop in terms of number and diameter. Using up to 20 years of monitoring data from three study sites, we developed a transition matrix model to predict the future number of saplings and their diameter distribution for mixed yellow birch ( Betula alleghaniensis Britton) – conifer stands. Our results show that partial cutting allowed yellow birch, red maple ( Acer rubrum L.), red spruce ( Picea rubens Sarg.), and balsam fir ( Abies balsamea (L.) Mill.) saplings to reach merchantable size faster and in greater numbers than in untreated control plots. We also found that fewer hardwood saplings (yellow birch and red maple) than softwood saplings (red spruce and balsam fir) were required to produce 1 m2·ha−1 of merchantable basal area after 20–40 years. Finally, our model provides a tool for forest managers to predict sapling development in mixed hardwood and softwood stands over a full cutting cycle.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".