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Record W4409360628 · doi:10.1139/cgj-2024-0570

The effects of desiccation crack and seasonal variation on hydrological response of compact clay embankment

2025· article· en· W4409360628 on OpenAlexvenueno aff
Arwan Apriyono, Yuliana Yuliana, Viroon Kamchoom‬, Anthony Kwan Leung, Apiniti Jotisankasa

Bibliographic record

VenueCanadian Geotechnical Journal · 2025
Typearticle
Languageen
FieldEngineering
TopicSoil and Unsaturated Flow
Canadian institutionsnot available
Fundersnot available
KeywordsLeveeGeotechnical engineeringGeologyDesiccationEnvironmental scienceEcology

Abstract

fetched live from OpenAlex

The presence of cracks significantly impacts the hydrological behaviour of clay embankments. This study aimed to enhance understanding of the complex interplay between the amount and propagation of desiccation cracks and seasonal variations. A full-scale embankment was constructed and equipped with an array of instruments, including pore water pressure, volumetric water content (VWC), and crack observer. The results suggested that continues cracks at shallow depths (0.5 m) exhibit significant seasonal fluctuations due to pronounced soil–atmosphere interactions, facilitating rapid water movement and substantial changes in crack width. In contrast, discontinuous cracks at intermediate depths (0.5 m) are less affected by seasonal changes, but they can propagate and connect over time due to repeated wetting and drying cycles. The crack intensity factor (CIF) above 0.4 m is highly sensitive to climatic variations, leading to pronounced fluctuations with changes in rainfall and dry conditions. The twofold increase in CIF values leads to a significant reduction in VWC (by 13.5%) at the depth of 0.25 m under the same atmospheric water balance. However, this effect is less pronounced at greater depths, such as 0.5 m, as discontinuous cracks are less effective in facilitating rapid drainage and moisture loss.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.011

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 head0.006
GPT teacher head0.209
Teacher spread0.203 · 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 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

Citations7
Published2025
Admission routes1
Has abstractyes

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