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Record W4388662273 · doi:10.1139/cgj-2023-0133

Effects of extreme drought–rainfall on slope failure mechanisms: centrifuge modelling

2023· article· en· W4388662273 on OpenAlexvenueno aff
Haiyi Zhong, Yikai Wang, Shuai Zhang, Qi Zhang, Charles Wang Wai Ng

Bibliographic record

VenueCanadian Geotechnical Journal · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicLandslides and related hazards
Canadian institutionsnot available
FundersResearch Grants Council, University Grants Committee
KeywordsCentrifugeGeotechnical engineeringSlope stabilitySlip (aerodynamics)LandslideGeologyCrestShear (geology)InstabilityEnvironmental scienceEngineeringMechanics

Abstract

fetched live from OpenAlex

The extreme drought–rainfall seesaw is projected to occur with an increasing frequency. However, there still lacks a thorough understanding of its impacts on slope behaviour, in which desiccation crack plays a key role. To address this issue, a centrifuge test was conducted to investigate the effects of drought-induced desiccation crack on slope instability under extreme rainfall. During the test, the non-cracked slope was firstly subjected to extreme rainfall with 100-year return period. Subsequently, a long-term drying was applied to induce desiccation crack, and hence forming a cracked slope. The cracked slope is then subjected to an identical extreme rainfall. The non-cracked slope only exhibits swelling deformation, whereas for the cracked slope, a slip surface (2 m in depth) is clearly observed to initiate from one deep crack at crest. The global sliding failure of the cracked slope is mainly related to preferential flow, which could result in soil shear strength reduction.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.010
GPT teacher head0.192
Teacher spread0.181 · 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 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

Citations13
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Geotechnical Journal→Same topicLandslides and related hazards→French-language works237,207→