Cadaver transport in large river systems: winter case study in the South Saskatchewan River
Bibliographic record
Abstract
An average of 518 drownings occur in Canada per year but few studies measured post-mortem submersion for cadavers and those studies are limited to warm areas of Europe and US. In our current study we deployed a pig cadaver in the South Saskatchewan River system in winter to monitor distance traveled and timing under ice conditions using radio-telemetry. We monitored accumulated degree days before bloating in the cadaver to estimate timing of emersion in winter drowning victims. Post-mortem submersion interval of the cadaver was relatively long (∼94 days) with an accumulated degree day estimate of ∼ 311 °C, and ultimately ∼189.9 km travelled. Our results suggest that winter drowning victims may remain at their point of disappearance for a substantial longer time compared to warmer regions; thereby providing adequate searching time to locate bodies shortly after disappearance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".