Modelling Head Injury due to Unmanned Aircraft Systems Collision: Crash Dummy vs Human Body
Bibliographic record
Abstract
Recent developments in the concept of UAS operations in urban areas have led to risk concerns of UAS collision with human. To better understand this risk, head and neck injuries due to UAS collisions have been investigated by different research teams using crash dummies. Because of the limitations in biofidelity of a crash dummy, head injury level for a crash dummy impact may differ from the human body impact. Therefore, the aim of this paper is to investigate differences in head and neck injuries subject to UAS collision with an often used crash dummy and a human body. To perform such investigation, multibody system (MBS) models have been used to simulate UAS impacts on validated models of the crash dummy and the human body. The findings confirm the moderate risks of head and neck injuries that have been reported. However, neck load differs significantly between the crash dummy model and the human body model, and the human body model sustains larger head injury but smaller neck injury compared to the crash dummy model.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".