Crown reconfiguration and trunk stress in deciduous trees
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 (C D ) 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, C D , 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 C D values 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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 it