Mechanical Properties of Green Wood and Their Relevance for Tree Risk Assessment
Bibliographic record
Abstract
In a biological context, the mechanical properties as elasticity and strength of green wood, particularly as measured in the axial direction, influence the stability of trees against static loads (e.g., snow, ice, rain) and dynamic loads (i.e., wind). Extensive collections of data on mechanical properties are listed in three different catalogs edited in Canada, Great Britain, and the United States. A statistical analysis shows that the density of the wood is a major predictor for the mechanical properties as measured in axial direction. In this respect, conifers from temperate zones and deciduous trees both from temperate and tropical zones do not differ significantly from each other. A common, nearly linear relation between the modulus of elasticity and the density at 50% moisture content is found. Relationships between strengths in bending, compression, and shear and green wood density have ordinary least squares scaling exponents around 1.2, but can almost equally well be approximated by linear functions of wood density. Therefore, if the density of stem wood of a given tree is known from direct measurement and differs from the tabulated value, the values tabulated for mechanical properties can be corrected for by a simple rule of proportion. Pulling tests as tools for tree control are discussed with emphasis on how the method is based on the knowledge of the mechanical properties of green wood, and how wood density is measured.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".