Models of 3D crown structure for Scots pine (<i>Pinus sylvestris</i>) and silver birch (<i>Betula pendula</i>) grown in mixed forest
Bibliographic record
Abstract
The interplay between plant structure and functioning determines vital plant characteristics, such as the efficiency of light capture by the foliage, and thus continuous efforts have attempted to include plant architecture in plant growth models. We constructed two crown structure models enabling the generation of 3D trees using simple tree measurements as input for Scots pine ( Pinus sylvestris L.) and silver birch ( Betula pendula Roth.), boreal species growing in mixed stands. The data for the models were obtained from sample-based digitizing of 3D crown architecture accompanied by characterizations of stem, branch, and foliage volumes and biomass. A set of equations was used to predict local crown characteristics on the basis of local predictors and tree-level input variables such as tree height, crown height, diameter at breast height, and the degree of competition. The 3D tree model was realized within the LIGNUM tree modelling framework. Comparison with data showed that the model adequately predicts the distribution of biomass into different crown components as well as the distribution of foliage within the tree crowns. The applicability of the simulations is at its best when examining Scots pine and silver birch trees growing on medium fertile mixed stands in Finland.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".