Spatial and Temporal Variation of Surf Drownings in the Great Lakes: 2010–17
Bibliographic record
Abstract
Vlodarchyk, B.; Olivito, A., and Houser, C., 2019. Spatial and temporal variation of surf drownings in the Great Lakes: 2010–17. Journal of Coastal Research, 35(4), 794–804. Coconut Creek (Florida), ISSN 0749-0208.Drownings on the Great Lakes are an emerging public health issue in the United States and Canada, but little is known about the physical and human dimensions of drowning and associated coastal hazards in this region. This study describes spatial and temporal variation of surf-zone drownings on the Great Lakes between 2010 and 2017 with respect to the demographics of the drowning victims, proximity to population centers, and interannual variations in the regional climate. A total of 391 drownings were reported on the Great Lakes during this period, but there is considerable variability in the number of drownings among the lakes and from year to year. The largest number of drownings occurred on Lake Michigan (n = 207; 53%), with most drownings concentrated along the southern end of the lake, near large population centers. The number of drownings in the other lakes ranged from 67 in Lake Erie (17%) to 27 (7%) in Lake Superior. Most drownings during this period occurred in the summer months of June, July, and August, with a disproportionate number occurring on a Sunday (n=102; 26%). Most drownings involved males between 10 and 30 years old (n = 167; 43%), with males between 15 and 20 years old accounting for the largest proportion of drownings (n = 69; 18%). Most drownings occurred during periods when wind speeds (a proxy for wave height) were relatively weak (2–5 m s-1), although there were many drownings when winds ranged from 5 to 10 m s-1. The number of drownings in each year was found to be dependent on air and water temperature, annual precipitation, and the concentration of ice in the previous winter. The identified relationships suggest that the number of drownings is expected to increase in the future with a warming climate, which, in turn, suggests a need for increased education and lifesaving programs in the region.
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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 | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".