Site conditions and definition of compositional proportion modify mixture effects in <i>Picea abies</i> – <i>Abies alba</i> stands
Bibliographic record
Abstract
For most forest types in the European Alps, little is known about mixture effects on stand productivity. The comparability of studies on mixture effects often suffers from the open methodological question of whether the results depend on the definition of compositional proportion. In this study, data from the Swiss National Forest Inventory were used to investigate how the growth of Norway spruce (Picea abies (L.) Karst.) and silver fir (Abies alba Mill.) is modified by the admixture of the other species and if the mixture effect depends on site, climate, age, or stand density. Stocking proportion (proportion by area) as well as the proportion of relative density index, stem number, basal area, stem volume, and aboveground biomass were used to define compositional proportion, and the results were compared. At low-quality sites, Norway spruce grew faster in basal area as its relative share of composition increased, but this pattern diminished as the site quality increased. At cooler sites, silver fir grew faster as its share of composition decreased, but the pattern reversed at warmer sites. Overyielding was predicted only for 16% of the 679 sites used for this study. Beneficial effects of species mixture were overestimated when species-specific stocking potentials were not considered in the definition of compositional proportion.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 teacher head, 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".