Bibliographic record
Abstract
Mixed-species stands are on the advance in Europe. They fulfil many functions better than monocultures. Recent papers show that mixed stands can have higher yields, but it remains open whether mixed stands simply grow faster along the same self-thinning lines as pure stands or have higher maximum stand densities. We analyzed the effect of species mixing on maximum density based on triplets of pure and mixed stands at approximately maximum density. Most considered mixtures include Norway spruce (Picea abies (L.) H. Karst.). We show that (i) in mixed stands, maximum density is, on average, 16.5% higher than in neighbouring pure stands, and (ii) species mixtures with Norway spruce exceed densities of pure stands by 8.8%, on average. For individual species mixtures, we find a significant density effect of +29.1% for Norway spruce mixed with European larch (Larix decidua Mill.) and +35.9% for Scots pine (Pinus sylvestris L.) in association with European beech (Fagus sylvatica L.). No significant links with stand variables such as age and mean tree size and site fertility were found. The results indicate that species mixing substantially increases stand density, indicating a higher carrying capacity caused by a higher supply and use efficiency of resources. The implications for inventory, silviculture, and forest modelling are discussed.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".