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Determining Beach User Knowledge of Rip Currents in Costa Rica

2017· article· en· W2777221639 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Coastal Research · 2017
Typearticle
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsRip currentGeographyHazardFisheryShoreEcology

Abstract

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Llopis, I.A.; Echeverria, A.G.; Trimble, S.; Brannstrom, C., and Houser, C., 2018. Determining beach user knowledge of rip currents in Costa Rica.Rip currents account for over 50 drownings a year in Costa Rica, with most drownings involving young single male students at beaches in close proximity to San Jose. The hazard posed by a rip current in Costa Rica and elsewhere depends in part on beach user knowledge of the rip current hazard and their ability to identify the situations in which there is the potential for drowning or need for rescue. This study describes the results of beach user surveys (n = 171) completed in English and Spanish at Jacó Beach on the central Pacific coast and Cocles Beach on the Caribbean coast of Costa Rica in 2013 and 2014. Rip current knowledge amongst national (domestic) and foreign tourists was estimated on both beaches. Results suggest that the amount of time spent in the water, activities on the beach, and self-assessment of swimming abilities help to explain why more males drown despite similar populations of males and females at both beaches. The personal and group behaviors that increase the potential for drowning are exacerbated by problems with the rip current warning system used at each beach. Approximately 58% of respondents did not observe the warnings, and 41% self-reported not changing their behavior after observing the sign, with 40% of respondents noting that the messages on the signs were confusing. Results of the study highlight the need to design and employ more effective warning signs, to set up a national certified lifeguard corps, and to plan educational activities aimed at those who are at greatest risk to drowning. This assessment and the identified need to develop a national policy can serve as a model for other countries in Central America and elsewhere where rip current–related drownings are a public health concern.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.114
Threshold uncertainty score0.664

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.209
GPT teacher head0.537
Teacher spread0.329 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it