Informing mixed conifer gap-based silviculture with growth dynamics of regeneration and gap expansion trials
Bibliographic record
Abstract
Where frequent fires are the primary driver of gap dynamics, gap-based silviculture can be deployed to restore fire-maintained structures. We studied the growth of regeneration for six species in a western US mixed conifer forest, following 12 years of growth across a range of gap sizes (0.1–1 ha). We found that severe edge effects limited growth within 3 to 4 m of surrounding canopy trees. Using an edge zone where competition is expected to include more moderate levels of growth suppression, we compared 12-year growth in edge zones versus interior zones. Ponderosa pine was the tallest among edge trees, followed by giant sequoia, Douglas-fir, sugar pine, incense-cedar, and white fir. Following experimental gap expansions (i.e., femelschlag harvest), we observed that increases in light availability were substantial along southern edges, whereas northern edges had no increase. Saplings in edge zones grew substantially in response to expansion. Our results demonstrate that using gap-based silviculture can create coarse-scale heterogeneity while regenerating all species, including shade-intolerants in smaller gaps. Gap expansions can be used to restore some of the structural complexities that fire historically maintained. Colonnades of tree-free space may enhance objectives of heterogeneity and fuel discontinuity that frequent fires used to maintain.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".