Prescribed burning of canopy gaps facilitates tree seedling establishment in restoration of pine-dominated boreal forests
Bibliographic record
Abstract
Because many currently protected forests are former timber production areas, restoration activities are often used to re-establish their natural structures. In this experimental study, we monitored the establishment of tree seedlings in previously managed but currently protected Scots pine (Pinus sylvestris L.) dominated stands in boreal forests 5 years after restoration measures. The study included eight study areas (115 sample plots) in southern Finland. We compared seedling abundance between five study groups: untreated control forest, unburned canopy gap, burned full-canopy forest, burned canopy gap, and thinned and burned forest. Density of tree seedlings was highest in burned canopy gaps (mean 25.4 seedlings/100 m2 compared with 6.0 seedlings/100 m2 in control sites). In particular, birch (Betula spp.) and Scots pine were significantly more abundant within burned canopy gaps than in unburned gaps. We conclude that opening within-stand canopy gaps, especially in combination with prescribed burning, can be useful in forest restoration as the gaps diversify age-class structure and tree species composition of single-cohort pine stands.
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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) | 0.000 | 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".