Comparison of Vehicle Structural Integrity and Occupant Injury Potential in Full-frontal and Offset-frontal Crash Tests
Bibliographic record
Abstract
The frontal crash standard in the USA specifies that the full front of a vehicle impact a rigid barrier. Subsequently, the European Union developed a frontal crash standard that requires 40 percent of the front of a vehicle to impact a deformable barrier. The present study conducted paired crashes of vehicles using the full-frontal barrier procedure and the 40 percent offset deformable barrier procedure. In part, the study was to examine the feasibility of adding an offset test procedure to the frontal crash standard in the USA. Frontal-offset and full-frontal testing was conducted using both the mid-size (50th percentile male Hybrid III) and the small stature (5th percentile female Hybrid III) dummies. Five vehicle models were used in the testing: Dodge Neon, Toyota Camry, Ford Taurus, Chevrolet Venture and Ford Contour. In the crash tests, all dummies were restrained with the available safety belt systems and frontal air bags. Both dummy sizes often exceeded the allowable lower leg injury criteria both in the offset tests and the full-frontal tests. Lower leg injury occurrences in real world crashes, based on National Automotive Sampling System data, show that many lower leg injuries occur in both fullfrontal and offset type crashes. The results also showed that vehicles with a stiffer occupant cage did well in the offset test. In addition, the test data suggested that the small female dummy may be exposed to a high risk of neck injury if positioned close to the deploying air bag.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".