Reconstruction Tests Design to Support the Correlation of Real Injuries with Dummy Readings
Bibliographic record
Abstract
From the many sub-tasks of the four study areas of the EC CASPER project, this paper presents the following point: • Child protection improvements as a result of accident reconstructions and development of injury risk curves. The first step in achieving this aim was to collect real world in-depth road accident data involving restrained children, with injuries systematically coded using the AIS (Abbreviated Injury Scale, AAAM 1998). This activity identified the priority body regions to be protected (therefore requiring injury risk curves) and provided cases to be reconstructed in full scale crash tests. In such reconstructions dummy readings were correlated with the occupants' injuries in the real accident to develop injury risk curves (after validation checks for crash severity and dummy kinematics). At the same time, online and field surveys were carried out to identify the safety of children when travelling in cars. These sociological studies provided information to identify the misuses of the Child Restraint Systems (CRS), resulting in new dynamic testing programs for their evaluation. The integration of these two activities resulted in the development of the criteria for selecting accident cases that would provide valuable information for the injury risk curves and which were technically feasible in crash testing laboratories. In order to select accident cases that would provide valuable information for the injury risk curves - a good spread across the whole spectrum of the AIS injury assessment (AIS1 - AIS6) - a case selection criteria was used that favoured more severe accidents in terms of injury severity or low injury severity accidents with high crash severities. The cases put forward for reconstruction had to be technically feasible in crash testing laboratories. Additionally, the signals captured from the new abdominal sensors, developed in the project, provided information for the injuries prediction in that area of the body. The present document was written before the end of the project so some of the references, such as results and conclusions are preliminary. However, it was possible to identify the child safety protection problems, based on the results of the sociological survey. The final results for the improvement of the Injury Risk Curves will be known when the final reports and models are released.
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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 | 0.011 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.037 | 0.009 |
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".