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Record W3214479189 · doi:10.32920/ryerson.14648730.v1

Experimental and finite element analysis of articular cartilage

2021· preprint· en· W3214479189 on OpenAlexaff
Abdolreza Karami

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMedicine
TopicOsteoarthritis Treatment and Mechanisms
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsFinite element methodDisplacement (psychology)Experimental dataArticular cartilageStructural engineeringCartilageMaterials scienceStress (linguistics)Ultimate tensile strengthDeformation (meteorology)Cervical spineJoint (building)Test dataTensile testingBiomechanicsBiomedical engineeringComputer scienceComposite materialEngineeringOsteoarthritisMathematicsAnatomyMedicineSurgeryPathology

Abstract

fetched live from OpenAlex

In the clinical field, articular cartilage has an important role in performance of most joints in the human body. In the present study, articular cartilage was excised from the knee joint of a bovine and tested in a tensile testing machine. The data obtained from the test was used to calculate the material properties of the cartilage. The material properites obtained from the experimental work were validated against the published experimental results in the literature. As the values from the experiment were in satisfactory agreement with the published data, it was concluded that the test protocol used in the experiment provides reliable data. Next, the material properties were implemented in finite element model of C3-C4 of cervical spine to examine if the finite element analysis can provide an accurate prediction of the behavior of the cervical spine. The stress and displacement results of the finite element analysis were consistent with the reported data in the literature. The location and the amount of the maximum stresses were examined and compared with the failure point of the materials.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.017
GPT teacher head0.278
Teacher spread0.261 · 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 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

Citations1
Published2021
Admission routes1
Has abstractyes

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