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Record W2058618005 · doi:10.2136/sssaj2000.6451554x

Measuring Hydraulic Properties Using a Line Source I. Analytical Expressions

2000· article· en· W2058618005 on OpenAlexafffund
Z. Fred Zhang, R. G. Kachanoski, Gary W. Parkin, Bingcheng Si

Bibliographic record

VenueSoil Science Society of America Journal · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil Moisture and Remote Sensing
Canadian institutionsUniversity of GuelphUniversity of SaskatchewanMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaOntario Ministry of Agriculture, Food and Rural AffairsUniversity of Guelph
KeywordsHydraulic conductivityReflectometryLine sourceInfiltration (HVAC)Soil scienceSaturation (graph theory)Pressure headInverseHydraulic headTRACERUniquenessMathematicsEnvironmental scienceTime domainMechanicsMathematical analysisGeologySoil waterGeotechnical engineeringPhysicsThermodynamicsGeometryOpticsComputer science

Abstract

fetched live from OpenAlex

In situ measurement of soil hydraulic properties remains a challenge. This study presents new analytical expressions for estimation of soil hydraulic properties below a surface line source by means of multi‐purpose time domain reflectometry (TDR) probes and existing quasi‐analytical, steady‐state solutions for infiltration from a surface line source. Inverse procedures are used to estimate the inverse macroscopic capillary length scale, α, and the hydraulic conductivity at saturation, K s , from pressure head (ψ), water storage ( W ), and conservative ionic tracer travel time ( T ) measured via multi‐purpose TDR probes placed at several depths below a line source with constant flux of water. Soil water content at saturation, θ s , can also be estimated if prior information is available. The parameter and spatial sensitivities of each solution were calculated by means of sensitivity coefficients. The uniqueness of possible combinations of measurements to estimate α, K s , and θ s was tested by means of two‐dimensional response surfaces. Significant correlation exists between K s and θ s , and thus it is not possible to estimate both K s and θ s by globally minimizing the objective function. Combination approaches with W (i.e., ψ and W , T and W , or ψ and W and T ) give unique estimates of α and K s if either θ s is known or prior information on θ s is available.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.719
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.003
Scholarly communication0.0000.001
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 head0.037
GPT teacher head0.248
Teacher spread0.211 · 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.

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

Citations22
Published2000
Admission routes2
Has abstractyes

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