An investigation into the head and neck injury potential of three-year-old children in forward and rearward facing child safety seats
Bibliographic record
Abstract
This research focuses on the injury potential of children in forward and rearward facing child restraint seats in frontal collisions. Experimental sled tests were completed following the guidelines outlined in the Federal Motor Vehicle Safety Standard 213 using a Hybrid III 3-year-old dummy in a convertible forward/rearward facing child restraint seat. The seat was equipped with a five point child safety belt and the experimental test was completed in the forward facing configuration. The Hybrid III 3-year-old dummy was equipped with three uniaxial accelerometers arranged in mutually perpendicular directions in the head and chest. A numerical model employing a subset of the apparatus used in the forward facing experimental sled test was developed and numerically simulated using LSDYNA. To verify the numerical simulations, the head and chest accelerations were compared to the experimental findings and it was observed that a reasonable correlation between the data existed. Further numerical simulations were completed to investigate the influence of positioning the 3-year-old dummy in the rearward configuration on the head and neck injury potential during frontal crash. Through an analysis of injury criteria, using neck loads and head accelerations, it was observed that the rearward facing child dummy sustained significantly lower levels of neck injury criteria while exhibiting similar levels of the head injury criteria as the forward facing 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) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".