The demographic structure of European aspen (Populus tremula) populations in managed and old-growth boreal forests in eastern Finland
Bibliographic record
Abstract
European aspen ( Populus tremula L.) is a keystone species for biodiversity in boreal forests. However, large aspen have largely been removed from managed forests, whereas regeneration and the long-term persistence of mature trees in protected areas are matters of concern. We recorded the numbers of mature (≥20 cm diameter) aspen in old-growth and managed forests in eastern Finland, based on a large-scale inventory (11 400 ha, 36 000 living and dead trees). In addition, saplings and small aspen trees were surveyed on thirty-six 1 ha sample plots. The average volumes of mature living and dead aspen were 4.0 and 1.3 m3/ha in continuous old-growth forests and 0.2 and 0.6 m3/ha in managed forests, respectively. These results indicate that large aspen trees in managed forests are a legacy of the past, when forest landscapes were less intensively managed. We conclude that the long-term persistence of aspen in protected areas can only be secured by means of restoration measures that create gaps large enough for regeneration to occur. More emphasis should be given to sparing aspen during thinning and to retaining mature aspen during regeneration cutting in managed forests.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".