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Use of X-ray CT scan to characterize the evolution of the hydraulic properties of a soil under drainage conditions

2016· article· en· W2415819199 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueGeoderma · 2016
Typearticle
Languageen
FieldEngineering
TopicSoil and Unsaturated Flow
Canadian institutionsInstitut National de la Recherche ScientifiqueUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHydraulic conductivityDrainageSoil scienceGeotechnical engineeringPorosityPedotransfer functionVadose zoneFractal dimensionGeologyEnvironmental scienceSoil texturePorous mediumSoil waterFractalMathematics

Abstract

fetched live from OpenAlex

Characterization of soil hydraulic properties is essential for modelling water flow and solute transport in the vadose zone. These properties are often assessed assuming that the soil is a non-deformable (rigid) porous medium. However, under real conditions, such as those found in agricultural systems, the soil is constantly exposed to external stresses induced by farm machinery and by wetting and drying cycles, which constantly modify the soil hydraulic properties. The main objective of this work was to develop a methodological framework based on X-ray CT scanning to predict the spatio-temporal evolution of the hydraulic properties of a soil under drainage conditions. The methodological framework combines the particle size distribution of a soil and the fractal dimension of its porosity obtained from X-ray CT scans to predict the saturated hydraulic conductivity and volumetric deformation of a soil column. The results show that the proposed framework provides a realistic description of the spatio-temporal evolution of the hydraulic properties of a soil during the drainage process.

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.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.498
Threshold uncertainty score0.160

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 head0.022
GPT teacher head0.190
Teacher spread0.168 · 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