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Record W4411656639 · doi:10.51847/os3hqohe4m

10.51847/os3HQohE4M

2000· article· en· W4411656639 on OpenAlexvenueno aff

Bibliographic record

VenueTime to knit · 2000
Typearticle
Languageen
FieldEngineering
TopicMechanical Engineering and Vibrations Research
Canadian institutionsnot available
Fundersnot available
KeywordsFinite element methodTibiaVibrationStructural engineeringMaterials scienceEngineeringMedicinePhysicsAcousticsAnatomy

Abstract

fetched live from OpenAlex

Nowadays, the study of bones that are compound materials featuring different characteristics in various body parts has been proposed as a novel subject in mechanic engineering and biomechanics.The present study tends to analyze the free vibrations (attainment of natural frequencies) of a specimen of cow fibula (considering the constraints on access to real human bone in Iran).At first, a 3D finite element model of the cow fibula was prepared using CT-scan images (the model is created by MIMICS software) following which the model was transferred to Abaqus Software to be further processed.In the beginning, the characteristics of the bone material is specified in the form of elastic inhomogeneous isotropic (based on density-elasticity relations offered by Carter, Keller and Morgan) (discrete model) and, then, the properties of the materials were inserted in a continuous manner for the individual bone parts following which the natural frequencies were acquired.Next, the effect of both of the models on the vibration attributes of the bone specimen was evaluated and the obtained result were compared with the laboratory results and it was made clear that the approach shift from discrete to continuous provides for obtaining more acceptable results (closeness of the answers to the experimental numbers) and it was found out that Morgan's relations provide for laboratory results closer to the real data.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.893
Threshold uncertainty score0.329

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)0.9850.972

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 head0.007
GPT teacher head0.187
Teacher spread0.180 · 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; both teacher heads agree on what is shown here.

Study designOther design
Domainnot available
GenreOther

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

Citations0
Published2000
Admission routes1
Has abstractyes

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