Crown reconfiguration and trunk stress in deciduous trees
Bibliographic record
Abstract
In light of the risk of litigation following damage related to tree failure in urban and suburban settings, more empirical data related to tree risk assessment are needed. We measured drag and drag-induced bending moment (M) and calculated drag coefficient (CD) and trunk stress (σ) for three deciduous trees at wind speeds up to 22.4 m/s. We measured the modulus of rupture (MOR) of wood samples from trunks and calculated the factor of safety (SF = MOR / σ) for each tree. We also investigated which tree morphometric variables best predicted drag and M and whether simple two- and three-dimensional shapes accurately represented actual tree crowns. Drag, CD, M, σ, and SF differed among species in accordance with physical parameters. More massive trees experienced greater drag and M, but σ was greater for trees with smaller trunk diameters. Tree mass reliably predicted drag and M; crown dimensions, including crown area, were less reliable predictors. Crown reconfiguration varied only slightly among species, and CDvalues were similar to previously reported values for trees of similar size. Our study has important applications for practitioners who manage tree risk, particularly the critical wind speeds and percentage of trunk cross-sectional area that could be decayed before trunk failure.
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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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 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".