A Comparison of the Head and Neck Injury Parameters on a TNO P3 and a Three-year-old Hybrid III Child Dummies From Numerical Simulations
Bibliographic record
Abstract
This study focuses on the behaviour of child dummies, namely a 3-year-old Hybrid III and a TNO P3, in terms of head and neck injury potential in forward and rearward facing child safety seats in frontal vehicle crash. Numerical simulations were conducted using a moderate acceleration pulse acquired from the National Transportation Biomechanics Research Center database with a closing speed of 41 km/h. A finite element model incorporating a three-year-old Hybrid III dummy, in a five-point convertible child safety seat was developed and the prescribed acceleration pulse was simulated using LS-DYNA. A multi-body dynamic simulation, utilizing the identical acceleration pulse, was completed for the three-year-old P3 dummy in a four-point convertible child safety seat using MADYMO. Similarities and differences were noted in the numerical observations for both the P3 and Hybrid III dummies which are presented within the paper. Peak values of the neck injury criteria, calculated from observations from the upper neck load cell in the forward facing configurations, for both the Hybrid III and P3 dummies were approximately 0.5. Values of the head injury criteria, calculated using a window of 15 ms, ranged from 20 to 70. For both the child dummies, neck and head injury criteria values were substantially reduced for the rearward facing configurations when compared with the forward facing results.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".