Mechanical response of the real tree root architecture under lateral load
Bibliographic record
Abstract
The impact of climate change on tree stability is often associated with a higher risk of windthrow due to higher frequency and greater magnitude of extreme climatic conditions. Higher lateral loads due to an increase in maximum wind and rainfall reduce tree anchorage because of a decrease in soil matric suction and consequently the overall strength in the system of trunk, root, and soil. This study compared the mechanical response of trees with different root architectures using static loading tests conducted in the field and numerical analysis of laser-scanned root systems. For this case, mature trees of Khaya senegalensis (Desr.) A. Juss., Samanea saman (Jacq.) Merr., and Syzygium grande (Wight) Wight ex Walp. were tested and analyzed. The root system models consisted of root system architectures obtained using 3-D laser scanning. A parametric analysis was conducted by varying the modulus of elasticity of the soil (Es) from 2.5 to 25 MPa, and the results were compared with those of the static loading tests to obtain the overall mechanical responses of the root–soil systems. The results showed important dependencies of the mechanical responses of the root–soil system on the root architecture in withstanding the lateral load. The numerical models also allowed estimation of the effective leeward and windward anchorage zones with different soil elastic moduli and rooting architectures to define the extent of the tree root protection zones.
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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 | 0.000 |
| 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) | 0.001 | 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".