Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.946 | 0.935 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".