Correspondence of Beach User Perception, Lifesaving Strategies and Rip Currents at Brackley Beach and Cavendish Beach Prince Edward Island
Bibliographic record
Abstract
Rip currents are an emerging public health hazard, with the potential to have serious socioeconomic impacts on coastal communities globally. Rip currents, colloquially known as rips, are common along the north shore of Prince Edward Island, and are associated with a transverse bar and rip morphology that tends to develop as the bars migrate landward following winter storms. The rip morphology tends to develop at the height of the economically important tourist season. Several drownings have occurred within and adjacent to Prince Edward Island National Park (PEINP) in recent years, and they have been attributed to rip currents at or near the primary access points to the beach. Whether someone drowns or is in need of rescue depends on a complex interplay between the physical presence of the rip, the management of the specific beach and the perceptions of the beach user. This study is the first to examine the relationship amongst the presence and location of rips, beach user intentions and behaviour, and the lifeguard-defined hazard level. Beach user surveys were administered over a four-week period at the popular tourist beaches of Cavendish Beach and Brackley Beach within PEINP, with an objective to analyze the spatial and temporal variation of beach users with respect to their understanding of the rip hazard, warnings posted by the lifeguards and quasi-permanent rips at the sites. Results suggests that the intention of a beach user to be safe at the beach may not accurately reflect their actions. Many beach users surveyed in this study did not pay attention to beach warnings and appear to be influenced by other factors that confirmed their assumption that the beach was safe. Those who did not observe the sign tended to sit further away from the access point compared to those who saw the signs. Tourists were also found to be at higher risk of needing rescue or drowning caused by rip currents due to their lack of rip knowledge and their inability to recall the location and colour of warning flags and rip signage. Further research into the correspondence of beach user perception, lifesaving strategies and rip currents can help reduce the number of drownings related to rip currents and other surf hazards through the effective deployment of lifesaving strategies and increase public confidence in rip hazard predictions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".