Water‐related fatalities: An examination of body displacement and recovery patterns in British Columbia, Canada
Bibliographic record
Abstract
Early recovery of human bodies from the water requires an understanding of how a body acts in the water. However, there is currently a lack of baseline data surrounding body movement in British Columbian (B.C.) waters. This study aims to assist Canadian response agencies with understanding and predicting body movement in outdoor waterbodies in B.C. One hundred and eighty-six water-related fatalities in B.C. waters, including lakes, rivers, and the coastal Pacific Ocean, were examined to determine the recovery times and displacement patterns of submerged decedents. Cases between 2010 and 2021 were extracted from the Police Records Information Management Environment (PRIME-BC) for analysis. Most deaths were unintentional, followed by suicide and homicide, and most often occurred in rivers, followed by lakes and the ocean. Regardless of waterbody, the first day was the most successful recovery period, with decedents most often recovered close to the incident location. Nearly 16% of individuals in this study were not recovered. Recovery success was greatest in lakes, followed by rivers and the ocean. Body displacement was the least in lakes, while rivers resulted in the furthest and most variable displacement. Low recovery success in the ocean is likely due to decedents being quickly displaced out of the search area, never to be found. The results of this study suggest that knowledge of body movement in outdoor aquatic environments remains incomplete. Further empirical research based on known data is necessary to continue improving prediction of body movement and increase early recovery success.
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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 | 0.004 | 0.007 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".