Predicting branch to bole volume scaling relationships from varying centroids of tree bole volume
Bibliographic record
Abstract
A novel “varying-centroid” method is presented for predicting whole-tree, aboveground stem volume (i.e., bole plus branch volume) to bole volume ratios from changes in the centroid of tree bole volume associated with branching of the bole. The method was derived from a simple fractal-like tree model based on a conceptualization of tree branching architecture by Leonardo da Vinci. The method recognizes that the centroid of bole volume (the point at which one half of bole volume is above and one half is below) is always lower than the centroid of whole-tree volume and that shifts in the centroid of bole volume should be predictably related to the size of a tree’s crown. The method assumes that branch-displaced bole volume profiles can be compared with reference bole profiles that are not significantly influenced by branching, at the centroid of bole volume, and that the magnitude of bole centroid displacement predicts the branch volume necessary to cause it. When the method was applied to hardwood trees representing diverse species, sizes, and stand conditions across Michigan, the centroid of bole volume was found to vary predictably with measurable tree crown attributes and bole plus branch wood to bole wood volume ratios were generally predicted within 10% of the true value using the new method.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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 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".