How do different thinning methods influence spatial tree diversity in mixed forest stands of planted Norway spruce (<i>Picea abies</i> L<i>.</i>) and naturally regenerated birch (<i>Betula</i> spp<i>.</i>) in southern Sweden?
Bibliographic record
Abstract
Forest biomass has become a viable alternative energy source for replacing fossil fuels, particularly after the European Union acknowledged its sustainability status. To reach zero net greenhouse gas emissions by 2045 in Sweden, new efficient methods of biomass extraction, such as geometrical biomass thinnings, are being explored and tested. These machine-based methods involve the extraction of above-ground biomass in narrow, 1–2 m-wide strips between extraction racks. While evidence-based optimization of biomass extraction mostly focuses on time- and cost-efficiency and on stand growth, criteria such as tree diversity are often overlooked. However, with ongoing climate change, tree diversity is crucial to strengthening the resilience and productivity of future forests, which also enhance the provision of ecosystem services and overall biological diversity. Therefore, we studied the effects of different biomass thinning strategies on spatial tree diversity in southern Sweden using nearest-neighbour summary statistics. We found scientific evidence that different geometrical designs of biomass thinning, especially in 1 or 2 m-wide strips, resulted in higher spatial tree diversity compared to conventional biomass thinning. Hence, in mixed conifer-broadleaved forests, biomass thinning in 1 or 2 m-wide strips is recommended for maintaining spatial tree diversity.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".