Structural changes in protected forests in Sweden: implications for conservation functionality
Bibliographic record
Abstract
Protected forest areas (PFAs) are key features of biodiversity conservation, and knowledge about long-term development is crucial in evaluating their efficiency and management needs. Longitudinal data on forest structure in PFAs is uncommon and often from small areas. Here we use data from the Swedish National Forest Inventory to study changes in more than 750 000 ha of PFAs over 60 years. Structures important for biodiversity, e.g., number of large trees and the volume of hard deadwood, including both standing and down wood, have more than doubled. The initial volume of deadwood, however, was very low. The overall tree species composition was stable over time, and only among the largest trees were there indications of a shift towards the late successional Norway spruce (Picea abies (L.) Karst.). Deadwood increased independent of species, size of wood, and site characteristics. This increase was positively related to the volume of living trees and forest age. We conclude that Swedish PFAs, in the absence of active management and under fire suppression at the landscape scale, develop structural components that are crucial for conservation of biodiversity. However, although tree species composition appears stable, present disturbance regimes in the PFAs are considerably different from those in naturally dynamic forests, which may have implications for long-term biodiversity maintenance.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".