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Record W4412699920 · doi:10.11159/ffhmt25.153

Determining the Diffusion Coefficient for Articular Cartilage Modelled As Homogeneous and Porous Material

2025· article· en· W4412699920 on OpenAlexvenueno aff
Anna Skorupa, Alicja Piasecka-Belkhayat

Bibliographic record

VenueProceedings of the ... International Conference on Fluid Flow, Heat and Mass Transfer · 2025
Typearticle
Languageen
FieldMedicine
TopicOsteoarthritis Treatment and Mechanisms
Canadian institutionsnot available
FundersSilesian University of Technology
KeywordsArticular cartilagePorosityHomogeneousDiffusionMaterials sciencePorous mediumEffective diffusion coefficientComposite materialOsteoarthritisThermodynamicsPhysicsMedicine

Abstract

fetched live from OpenAlex

Porous materials are defined as materials consisting of a 'matrix' in which the voids are filled with gas or liquid.Porous materials also include biological tissues, because they consist of dispersed cells isolated by voids (pores) through which nutrients flow to all cells in them.An example of such tissue is articular cartilage, in which the size of the pores is estimated to be between 2 nm and 6 nm.The study involved determining the diffusion coefficient value for a selected tissue (articular cartilage) during cooling, assuming a model for homogeneous and porous material.The results obtained confirm that the applied material model impacts on the values of the diffusion coefficient.Higher coefficient values were obtained for the model taking into account the porosity of the sample than for the homogeneous material model.In addition, a decrease in temperature causes a decrease in the diffusion coefficient value.The results obtained can be used in the future to analyse the behaviour of a biological sample during the cryopreservation 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.

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 score0.030
Threshold uncertainty score0.359

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 head0.015
GPT teacher head0.246
Teacher spread0.231 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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