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Record W3098466534 · doi:10.22215/etd/2020-14151

Investigations of Sliding-Induced Solute Transport in Articular Cartilage

2020· dissertation· en· W3098466534 on OpenAlexaff
Kathryn Culliton

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldMedicine
TopicOsteoarthritis Treatment and Mechanisms
Canadian institutionsCarleton University
Fundersnot available
KeywordsThermal diffusivityArticular cartilageMaterials scienceMass transferMass transportWork (physics)Compression (physics)DiffusionMass diffusivityCartilageComposite materialMechanicsThermodynamicsEngineeringAnatomyPhysicsMedicineOsteoarthritis

Abstract

fetched live from OpenAlex

Solute transport has not been studied in articular cartilage subject to sliding loads, as occurs in vivo.Therefore, the goal of this thesis is to provide a comprehensive investigation of solute transport under sliding loads specifically investigating enhancement to mass transfer and spatiotemporal changes to diffusivity within the tissue.The findings are contextualized by supplemental investigations comparing the results to commonly studied loading modes such as uniaxial compression, elucidating the role of the superficial region as a barrier to load induced solute transport, and the accuracy of nutrient diffusion models (single vs multi-layer) for characterizing diffusivity.This study demonstrates that mass transfer is higher for sliding compared to uniaxial compression reaching 4.4-fold at 2 hrs (p=0.002) and this can exceed the passive capacity of the tissue.In addition, this work demonstrates a spatiotemporal dependence of diffusivity within the tissue under sliding loads.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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)0.0020.000

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 head0.027
GPT teacher head0.269
Teacher spread0.242 · 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 designObservational
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
Published2020
Admission routes1
Has abstractyes

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