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Record W4410108168 · doi:10.1139/cgj-2024-0740

Root–soil load transfer mechanisms upon tree overturning: a closer look on the soil stress state evolution and root strength mobilisation

2025· article· en· W4410108168 on OpenAlexvenueno aff
Jun Zhu, Anthony Kwan Leung

Bibliographic record

VenueCanadian Geotechnical Journal · 2025
Typearticle
Languageen
FieldEngineering
TopicTree Root and Stability Studies
Canadian institutionsnot available
Fundersnot available
KeywordsRoot (linguistics)Geotechnical engineeringTree (set theory)Stress (linguistics)State (computer science)Environmental scienceGeologyMathematics

Abstract

fetched live from OpenAlex

Understanding the anchorage of a complex root system architecture (RSA) in soil upon tree overturning is vital to evaluate tree stability under lateral loads. Empirical correlations between root anchorage capacity and root morphological traits have been established, but the role of soil in these correlations has been ignored. This study developed and validated a three-dimensional finite element model and then used it to investigate the underlying mechanisms of root–soil load transfer mechanisms in terms of the evolution of soil stress states and root strength mobilisation during overturning. Two root traits—radial distance and embedded depth—influenced root anchorage capacity remarkably. The pattern of soil stress state evolution near taproots and laterals was remarkably different. Roots that displaced in the direction more aligned with the soil’s major principal stress were more effective to mobilise their strength to resist against overturning. The failure envelope defined by the normalised peak moment capacity in the x- and y-direction of the asymmetric RSA was elliptic, displaying anisotropic overturning under combined load conditions.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.704
Threshold uncertainty score0.822

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.194
Teacher spread0.187 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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