Personificación de Modelos de Elementos Finitos de Cuerpo Humano
Bibliographic record
Abstract
The goal of this study was to quantify the effect of improving the geometry of a human body model on the accuracy of the predicted kinematics for 4 post-mortem human subject sled tests. Three modifications to the computational human body model THUMS were carried out to evaluate if subject personification can increase the agreement between predicted and measured kinematics of post-mortem human subjects in full frontal and nearside oblique impacts. The modifications consisted of: adjusting the human body model mass to the actual subject mass, morphing it to the actual anthropometry of each subject and finally adjustment of the model initial position to the measured position in selected post-mortem human subject tests. A quantitative assessment of the agreement between predicted and measured response was carried out by means of CORA analysis by comparing the displacement of selected anatomical landmarks (head CoG, T1 and T8 vertebre and H-Point). For all three scenarios, the more similar the human body model was to the anthropometry and posture of the sled tested post-mortem human subject, the more similar the predictions were to the measured responses of the post-mortem human subject, resulting in higher CORA score.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".