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Record W2787659822 · doi:10.22215/etd/2014-10390

The Effect of Cracks and Geo-Morphology on Evaporation from Clay Soils.

2014· dissertation· en· W2787659822 on OpenAlexaff
Khalil Djalal

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicSoil and Unsaturated Flow
Canadian institutionsCarleton University
Fundersnot available
KeywordsSaturation (graph theory)Soil waterEvaporationGeotechnical engineeringMaterials scienceGeologyComposite materialMineralogySoil scienceMathematicsThermodynamicsPhysics

Abstract

fetched live from OpenAlex

Crack development in soils and the associated potential for higher evaporation rates are pertinent to many agricultural and engineering applications.Many researchers have attempted to model crack formation though none have succeeded to comprehensively capture it.Chertkov's model is a commonly cited theory for describing crack initiation and development.This theory uses only two independent parameters to model crack development, which makes it a promising tool for engineering applications.This research sought to verify Chertkov's model, as well as to analyze the coupled effect of crack growth and evaporation.A large scale (1.29 by 1.45 m by 0.09 m in thickness) clay drying test was carried out where crack growth, rate of evaporation and surface suction were monitored.Volume change and crack development were tracked using a 2-D laser scanner mounted on a scanning control mechanism, as well as by pixel analysis of surface photographs.A fractrographic analysis was also executed to determine the nature of soil deformation at crack tip.The drying test has shown that crack surfaces contributed at most to 6% of total evaporation before the onset of de-saturation and up to 63.4% after de-saturation.Results have also shown that Chertkov's model could successfully model crack growth, though the parameters had somewhat unrealistic values.While the fractographic analysis showed that some of Chertkov's model assumptions were violated, in particular that plastic strains appeared to be important, it is not clear whether these violations rule out this model as a useful tool.II

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0020.000

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 head0.003
GPT teacher head0.209
Teacher spread0.206 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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Same topicSoil and Unsaturated FlowFrench-language works237,207