MétaCan
Menu
Back to cohort
Record W2276066215 · doi:10.2136/vzj2015.01.0015

Limit of Anisotropic Hydraulic Conductivity Ratio of Homogeneous Granular Materials

2015· article· en· W2276066215 on OpenAlexafffund
Peijun Guo, Yaqian Liu, Dieter Stolle

Bibliographic record

VenueVadose Zone Journal · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicGroundwater flow and contamination studies
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAnisotropyPorosityPermeability (electromagnetism)Hydraulic conductivityMaterials sciencePorous mediumPerpendicularGranular materialMechanicsGeometryGeologyComposite materialPhysicsOpticsChemistryMathematicsSoil waterSoil science

Abstract

fetched live from OpenAlex

This paper presents a theoretical upper limit for the anisotropic hydraulic conductivity ratio of homogeneous granular materials with internal structure. On the basis of a volume averaging approach, a theoretical analysis is proposed to relate the permeability of granular material to the spatial distribution of pore spaces. For flow in granular materials, both the linear porosity in the flow direction and the areal porosity in the plane perpendicular to the flow direction are required to describe the directional variation of pore space. The permeability tensor is derived by considering the macroscopic momentum balance equation of the fluid in a porous medium. While the permeability is affected by the porosity and the pore space distribution, the permeability anisotropy ratio is dominated by the directional variation of the linear porosity. By accounting for the theoretical maximum and minimum porosity, it is shown that the upper limit of permeability anisotropy for homogeneous granular materials is approximately 2.5, even for very flat (or elongated) particles and pronounced preferential orientations.

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)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.000
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.028
GPT teacher head0.233
Teacher spread0.205 · 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 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

Citations4
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueVadose Zone JournalSame topicGroundwater flow and contamination studiesFrench-language works237,207