SMALL PUNCH TESTING: AN ALTERNATIVE TESTING TECHNIQUE TO EVALUATE TENSILE BEHAVIOR OF CORTICAL BONE
Bibliographic record
Abstract
The tensile properties of cortical bone are usually determined with the help of uniaxial tensile test which requires enough amount of bone material. Further, it is very complicated to examine the heterogeneity and anisotropy associated with the deformational properties of cortical bone with the help of uniaxial tensile test. Through this study, small punch testing has been proposed as an alternate technique to evaluate the deformational behavior of cortical bone utilizing optimum amount of bone material. The comparison between elastic modulus values obtained from tensile test and stiffness values obtained through small punch testing was done for validation. The values of these properties were found to be having a significant positive correlation with each other. The effects of bone density and compositional parameters on these properties were also found to be having a similar trend. It is observed through this study that stiffness values from small punch technique are having a similarity with elastic modulus values from uniaxial tensile testing. It is proposed that small punch testing technique can be used as an alternate to examine the deformational behavior of cortical bone.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".