Potentials of Natural Tree Regeneration after Clearcutting in Subalpine Forests
Bibliographic record
Abstract
Abstract Regeneration of interior mountain forests still is not adequately understood, although these forests are subject to intensified use over the last decades. We examined factors influencing the success of natural tree regeneration after harvesting in the Engelmann spruce–subalpine fir zone of the Monashee Mountains, British Columbia, Canada. Distance from the forest edge was an important factor for regeneration; at distances exceeding 70 m from the forest edge only 50% of plots showed sufficient natural regeneration to meet stocking targets compared with 90% of plots closer to forest edges. Seedling density and growth were superior in the more protected southern portions of clearcuts. Seedling growth was less in plots containing high cover of downed woody debris. There was no relationship between understory plant diversity or composition and tree seedling regeneration. However, cover of fireweed (Epilobium angustifolium) had a significant negative relationship with density but not growth of tree seedlings, particularly for lodgepole pine (Pinus contorta var. latifolia). Cover of fireweed decline substantially within the first 10 years after clearcutting. We conclude that natural regeneration of trees has potential to help achieve stocking targets and also to maintain natural diversity of tree species if spatial constraints, especially thresholds in clearcut size, are considered.
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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 | 0.001 |
| 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".