Effects of overstory tree density, site preparation, and ground vegetation on natural Scots pine seedling emergence and survival in northern boreal pine forests
Bibliographic record
Abstract
Natural regeneration is a commonly used forest regeneration method in northern Finland. It is not known, however, what would be the optimal overstory density and ground vegetation composition for seedling emergence and survival, and if site preparation is needed to accompany overstory density manipulation. We studied the effects of overstory density (unthinned control and thinning to 50, 150, and 250 trees·ha−1) and ground vegetation removal (mechanical site preparation with disc trenching) on the number of naturally germinated pine seedlings and survival of individual seedlings over a period of 8 to 11 years. Bare mineral soil was a superior seedbed compared to intact vegetation cover, even though the mortality rate was high on mineral soil. Greater cover of lingonberry, crowberry, and slash had a negative effect on seedling number. Seedling mortality was initially high (60% died during the first 2 years) but decreased throughout the first 5 years. The survival rate of seedlings located in the mineral soil of the upper part of the disc trencher track was twice as high as that of seedlings located in the lower part of the track. High coverage of hair mosses (Polytrichum spp.) was associated with poorer seedling survival. An overstory density of 50–150 trees·ha−1 with site preparation seems to be an efficient treatment to promote regeneration under these circumstances.
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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".