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Record W4384696400 · doi:10.1139/cgj-2023-0104

Partially drained uplift behaviour of plant roots in dilative soils

2023· article· en· W4384696400 on OpenAlexvenueno aff
Jun Zhu, Anthony Kwan Leung, Yu Wang

Bibliographic record

VenueCanadian Geotechnical Journal · 2023
Typearticle
Languageen
FieldEngineering
TopicTree Root and Stability Studies
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsGeotechnical engineeringSoil waterGeologyEnvironmental scienceSoil science

Abstract

fetched live from OpenAlex

A large volume of research reporting the pull-out behaviour of root systems is available, but no study has considered the effects of soil drainage. This work implemented a modified three-dimensional embedded beam element model in a finite element platform that solved model equations by using a fully hydromechanically coupled algorithm. The model was validated against published centrifuge pull-out tests on root analogues, and the validated model was then applied to study parametrically the influence of the ratio of uplift rate to soil hydraulic conductivity on pull-out behaviour. The results demonstrated that the model can well capture the prepeak behaviour of the root systems up to the peak pull-out resistance. The generation of negative pore–water pressure ( p e x) owing to soil dilation upon root–soil interfacial shearing was the major reason for increased pull-out resistances under partially drained conditions. Compared with other root systems, root systems with smaller branch angles and deeper branch depths mobilised considerably more significant plastic deviatoric strains in the soil in their vicinity, generating more negative p e x. Hyperbolic dimensionless backbone curves were derived to explain the transitional pull-out behaviours of root systems of different geometries under drainage conditions that ranged from fully drained to undrained.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.017
GPT teacher head0.226
Teacher spread0.209 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations15
Published2023
Admission routes1
Has abstractyes

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Same venueCanadian Geotechnical JournalSame topicTree Root and Stability StudiesFrench-language works237,207