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Record W4313855356 · doi:10.1139/cgj-2022-0475

Temperature effects on the hydraulic properties of unsaturated rooted soils

2023· article· en· W4313855356 on OpenAlexvenueno aff
Hao Wang, Rui Chen, Anthony Kwan Leung, Junwen Huang

Bibliographic record

VenueCanadian Geotechnical Journal · 2023
Typearticle
Languageen
FieldEngineering
TopicSoil and Unsaturated Flow
Canadian institutionsnot available
Fundersnot available
KeywordsSoil waterHydraulic conductivitySuctionWater retention curveWater retentionShrinkageGeotechnical engineeringWater contentSoil scienceWater potentialEnvironmental scienceMaterials scienceGeologyComposite materialThermodynamics

Abstract

fetched live from OpenAlex

Soil temperature of vegetated landfill earthen covers varies with time during their service, due to heat released by the degradation of municipal solid waste. In this study, an experimental campaign to capture soil water-retention curve (SWRC) and hydraulic conductivity function (HCF) of unsaturated rooted soils was conducted at different temperatures (25, 45, and 60 °C). Simplified evaporation tests were conducted by subjecting soil specimens to a prolonged drying period at maintained temperature. Based on the responses of matric suction and water content, SWRC and HCF were determined by the instantaneous profile method. It was found that increasing temperature reduced water-retention capacity, due to root shrinkage, thermal expansion of liquid phase, and reduction in forces of capillarity and adsorption. Soil hydraulic conductivity was enhanced by the increase in soil temperature due to reduction of water viscosity and potential preferential flow at root channels induced by root shrinkage. Temperature effects on SWRC and HCF of rooted soils are suction-dependent. A new nonisothermal SWRC model was developed to capture the dual effects of roots and temperature. By using the same set of calibrated parameters, the model demonstrates its ability to predict SWRCs reasonably well within the range of temperature investigated.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.241
Threshold uncertainty score0.540

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.011
GPT teacher head0.183
Teacher spread0.172 · 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 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

Citations42
Published2023
Admission routes1
Has abstractyes

Explore more

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