FORENSIC BIOMECHANICS -- TRANSDISCIPLINARY APPROACH IN THE COURT OF LAW
Bibliographic record
Abstract
The judicial systems of the United States and Canada very frequently utilize expert witnesses in engineering as well as most disciplines of science. Only in the last three decades biomechanists have been recognized and admitted to the courts as expert witnesses to provide opinions in the forensic biomechanics field. Thanks to the efforts of a few individuals, the science of biomechanics is now well accepted by the officers of the court systems of North America. Biomechanists possess the combined knowledge of engineering mechanics, biology, human anatomy, and physiology that makes it possible for them to reconstruct and analyze accidents of all kinds. In spite of the fact that the most obvious utilization of forensic biomechanics is in the area of analyses of injury mechanisms associated with motor vehicle accidents, there are other areas such as occupational, sports and recreational, slip/trip and fall accidents, and various product liability cases where forensic biomechanics expertise is required. In this paper, one litigation case is presented with some technical details and several other cases from different areas are briefly outlined. Because of obvious reasons, cases are presented in a generic format without referring to a particular company or organization.
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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 | 0.020 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.016 | 0.024 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.013 | 0.010 |
| Insufficient payload (model declined to judge) | 0.011 | 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".