Coarse woody debris in old <i>Pinus sylvestris</i> dominated forests along a geographic and human impact gradient in boreal Fennoscandia
Bibliographic record
Abstract
Coarse woody debris (CWD) was studied in old Pinus sylvestris L. dominated forests in three geographic regions in the middle boreal vegetation zone: (i) in Häme in southwestern Finland, characterized by a long history of forest utilization, (ii) in Kuhmo in northeastern Finland, with a more recent history of forest utilization, and (iii) in the Vienansalo wilderness area in northwestern Russia, characterized by large areas of almost natural forest. Within the geographic regions the measured 0.2-ha plots were divided into three stand types according to the degree of human impact: (i) natural stands, (ii) selectively logged stands, and (iii) managed stands. The results showed that compared with natural forests, forest management has strongly reduced both the amount and diversity of CWD. The highest total CWD volumes were found in the natural stands in Häme (mean 67 m3·ha1) and Kuhmo (92 m3·ha1) and in the selective logged stands in Vienansalo (80 m3·ha1), while the lowest CWD volumes were found in the managed stands in Häme (7 m3·ha1) and Kuhmo (22 m3·ha1). The duration of forest utilization also plays a role, as forests with short management histories (Kuhmo region) still carried structural legacies from earlier more natural stages of the forest. In addition to lower total CWD volumes, managed stands also largely lacked certain dead wood characteristics, particularly large dead trees and standing dead trees with structural diversity characteristics (such as stem breakage, leaning stems, and fire scars) when compared with natural and selectively logged stands. The CWD characteristics of stands selectively logged in the past were often comparable with those of natural stands, suggesting that old selectively logged stands can be of high value from the nature conservation point of view.
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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 | 0.001 |
| 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".