Comparing individual-tree growth models using principles of stand growth for Norway spruce, Scots pine, and European beech
Bibliographic record
Abstract
We examined the relationship between thinning intensity and volume increment predicted by four commonly used individual-tree growth models in Central Europe (i.e., BWIN, Moses, Prognaus, and Silva). We replicated conditions of older growth and yield experiments by selecting 34 young, dense plots of Norway spruce (Picea abies (L.) Karst.), Scots pine (Pinus sylvestris L.), and European beech (Fagus sylvatica L.). At these plots, we simulated growth, with mortality only, to obtain the maximum basal area. Maximum basal area was then decreased by 5% or 10% steps using thinning from below. Maximum density varied considerably between simulators; it was mostly in a reasonable range but partly exceeded the maximum basal area observed by the Austrian National Forest Inventory or the self-thinning line. In almost all cases, simulated volume increment was highest at maximum basal area and then decreased with decreasing basal area. Critical basal area, at which 95% of maximum volume increment can be achieved, ranged from 0.46 to 0.96. For all simulators, critical basal area was lower for the more shade-tolerant species. It increased with age, except for Norway spruce, when simulated with the BWIN model. Age, where mean annual increment culminated, compared well with yield tables.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".