Patterns of Trauma Induced by Motorboat and Ferry Propellers as Illustrated by Three Known Cases from Rhode Island*
Bibliographic record
Abstract
Understanding patterns of trauma is important to determining cause and manner of death. A thorough evaluation of taphonomy, trauma, and bone fracture mechanisms is necessary to reconstruct the circumstances of the death. This study examines the skeletal trauma caused by boat propeller strikes in terms of wound characteristics and location based on three cases from Rhode Island. These case studies review the traumatic characteristics caused by propeller injuries and highlight the anatomic regions most likely to sustain skeletal trauma. With this information, investigators may be able to identify propeller trauma even in severely decomposed remains. The discussion of boat propeller trauma also raises issues regarding how forensic anthropologists and forensic pathologists classify trauma (specifically blunt force vs. sharp) and highlights semantic issues arising in trauma classification. The study also discusses why these propeller cases should be classified as blunt trauma rather than sharp or chop/hack trauma. Ultimately, the authors urge consistency and communication between pathologist and forensic anthropologists performing trauma analyses.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".