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Record W2575767947 · doi:10.1680/jgele.16.00088

Improved estimate of the effective diameter for use in the Kozeny–Carman equation for permeability prediction

2017· article· en· W2575767947 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

VenueGéotechnique Letters · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicGroundwater flow and contamination studies
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersChina Scholarship CouncilMitacs
KeywordsPermeability (electromagnetism)Hydraulic conductivityPorosityPorous mediumMechanicsMaterials scienceGrain sizeGeotechnical engineeringMathematicsGeologyPhysicsComposite materialSoil scienceChemistrySoil water

Abstract

fetched live from OpenAlex

The Kozeny–Carman equation is widely used to predict the hydraulic conductivity or intrinsic permeability of granular porous media. This paper combines discrete-element modelling and finite-volume flow simulation to examine the Kozeny–Carman equation for permeability prediction of spherical grains with different grain size distributions. A comparison of the modelled permeability with the permeability predicted by the Kozeny–Carman equation shows that the existing equation predicts up to 20% smaller permeability for particle assemblies with non-uniform sizes, especially for gap-graded size distributions. An improved equation is proposed to calculate the effective diameter for use in the empirical Kozeny–Carman equation.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.562
Threshold uncertainty score0.236

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.263
Teacher spread0.240 · 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