Succession of understory vegetation in managed and seminatural Scots pine forests in eastern Finland and Russian Karelia
Bibliographic record
Abstract
The aim of this study was to compare the understory vegetation in chronosequences representing seminatural and managed Scots pine (Pinus sylvestris L.) forests on dryish heaths of the Vaccinium type. The data represent the middle boreal vegetation zone and were collected in eastern Finland and the Dvina area of Karelia, on both sides of the border between Finland and the Russian Federation. Species diversity and the coverage of the various life-form groups (ground lichens, mosses, liverworts, grasses, herbs, dwarf shrubs, and tree seedlings) were used to analyse the properties of the ground vegetation over the succession from open forest land to closed forest communities. The clearest differences in diversity between the seminatural forests mainly influenced by fire and managed forests occurred in the early stages of succession. Forest management appeared to increase the species richness at the beginning of succession. The seminatural stands were rich in Cladonia lichens and dwarf shrubs up until tree canopy closure; however, the abundance of mosses was lower in the seminatural stands. Forest management favoured an abundance of grasses, notably Deschampsia flexuosa, which was common after clear-cutting. The use of prescribed burning in silviculture could result in more natural vegetation succession dynamics in managed 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".