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Record W4404050201 · doi:10.70767/jebe.v1i2.225

Study on the Effect of Root Density on the Shear Strength of Root-Soil Composite: A Case Study of Cynodon Dactylon

2024· article· en· W4404050201 on OpenAlexaff
Lihua Jin

Bibliographic record

VenueJournal of environmental and building engineering. · 2024
Typearticle
Languageen
FieldEngineering
TopicTree Root and Stability Studies
Canadian institutionsEarl Haig Secondary School
Fundersnot available
KeywordsCynodon dactylonComposite numberRoot (linguistics)Shear strength (soil)MathematicsMaterials scienceAgronomyEnvironmental scienceComposite materialSoil waterSoil scienceBiologyLinguistics

Abstract

fetched live from OpenAlex

Since the Belt and Road Initiative was proposed, many slopes with poor stability have been formed in China's highway and railway infrastructure construction. In response to the national goals set during the 14th Five-Year Plan to strengthen the ecological security barrier and protect biodiversity, and in line with the philosophy that "lucid waters and lush mountains are invaluable assets," ecological protection technology has been widely applied in slope protection. However, current research and design have not considered the role of roots in retaining walls. This study uses an eco-friendly reinforced soil retaining wall project as an example to investigate the relationship between root density and the cohesion and internal friction angle of root-soil composites through direct shear tests. The results show that increasing root density significantly improves the cohesion and internal friction angle of root-soil composites. When the root density is 5%, the cohesion and internal friction angle increased by 40.57% and 51.41%, respectively, compared to the unreinforced soil sample, providing a theoretical reference for engineering design.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.419
Threshold uncertainty score0.440

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 integrity0.0000.000
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 head0.210
Teacher spread0.202 · 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 designObservational
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

Citations0
Published2024
Admission routes1
Has abstractyes

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