Commercial thinning stimulates natural regeneration in spruce–fir stands
Bibliographic record
Abstract
Understanding the response of tree regeneration following commercial thinning treatments can improve planning in managed forests dependent on natural regeneration. We used long-term commercial thinning experiments in eastern spruce–fir stands of Maine, USA, to test two hypotheses: (1) commercial thinning increases the density of tree regeneration and (2) tree regeneration density increases with increasing thinning intensity. A decade after thinning, densities of softwood and hardwood regeneration were 10 times greater in thinned stands than unthinned stands. The abundance of small softwood (0.11–0.60 m tall) was highest in lower intensity thinning treatments, whereas medium (0.61–1.40 m tall) and large (≥1.41 m tall to 8.90 cm diameter at breast height) softwoods increased proportionally with thinning intensity, a pattern related to a higher rate of recruitment in more open stands created by heavier thinning. Hardwood density generally increased with thinning intensity and developed into a significant component of the large size class. Softwood regeneration density was higher in older spruce stands than younger fir stands, which may be due to greater abundance of advance regeneration, higher residual stand mortality, and greater harvest disturbance in older spruce stands. However, acceptable softwood stocking was achieved in all replicates of thinning treatments. Therefore, in addition to providing higher individual-tree growth and merchantable yield, commercial thinning in eastern spruce–fir stands also increases regeneration density. The rate of recruitment also increased as thinning intensity increased, thus stimulating understory regeneration similar to that of a shelterwood establishment cut.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| 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 teacher head, 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".