Responses of the Q3, Hybrid III and a Three Year Old Child Finite Element Model Under a Simulated 213 Test
Bibliographic record
Abstract
This research focuses on the response of the Q3, Hybrid III 3-year-old dummy and a child finite element model in a simulated 213 sled test. The Q3 and Hybrid III 3-year old child finite element models were developed by First Technology Safety Systems. The 3-year-old child finite element model was developed by Nagoya University by model-based scaling from the AM50 (50 percentile male) total human model for safety. The child models were positioned in a forward facing, five-point child restraint system using Finite Element Model Builder. An acceleration pulse acquired from an experimental 213 sled test, which was completed following the guidelines outlined in the Federal Motor Vehicle Safety Standard 213 using a Hybrid III 3-year-old dummy, was applied to the seat buck supporting the child restraint seat. The numerical simulations utilizing the Q3, Hybrid III 3-year-old and the child finite element model were conducted using the explicit non-linear finite element code LS-DYNA. The main objective of this paper is to present the response behaviour of the child dummies in terms of head and neck injury potential in a simulated 213 test. Peak values of the neck injury criteria, calculated at the upper neck load cell for the Q3 and Hybrid III child dummies were approximately 1.21 and 0.96 respectively. Values for the head injury criteria, calculated using a 36 ms window, were observed to be 272 for the Hybrid III dummy, 260 for the child FE model and 337 for the Q3 child dummy.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".