No Speak, No Hear, No See: Improving Warning Systems for Rip Currents on the Great Lakes
Bibliographic record
Abstract
No Speak, No Hear, No See: Improving Warning Systems for Rip Currents on the Great Lakes By Hannah Burdett Rip current are a natural hazard that have received little attention within the Great Lakes. Without proper education and warning systems, unsuspecting beach users may enter the surf zone and place themselves in a dangerous situation. Understanding the danger to which a rip current pose on the Great Lakes and where and when rip currents tend to develop is critical for limiting drownings and rescues. The purpose of this study is to determine when existing warning systems in the United States and Canada are accurate. Specifically, an analysis was completed on the currently established rip current warning system presented by the National Weather Service and Environment Canada in regards to the amount of information that was provided, the geographic extent of the warning and whether the warning was heeded by beach users. A survey was completed to determine how many people have seen a rip current warning before going to a beach on the Great Lakes, and how well they comprehended the warning. Respondents were also asked about their understanding of the warning system and questioned about their knowledge of how to avoid or escape the hazard. GIS was also used to determine if there was a spatial correlation between drowning locations in the Great Lakes and the warnings provided by the National Weather Service and Environment Canada. Preliminary results suggest that the warning systems used in the United States and Canada lacks in both efficiency and effectiveness. Specifically, it is argued that the National Weather Service rip current warning system is not easily accessible to the public and provides inconsistent information in both space and time. Results will be used to improve the rip warning system used for the Great Lakes that is easily accessible as well as easy to comprehend, with the aim of reducing the number of deaths that occur each year in the Great Lakes.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".