Health Risk of Bathing in Southern California Coastal Waters
Bibliographic record
Abstract
Urbanized areas often discharge large volumes of contaminated waste into coastal waters, which may pose a health risk to bathers at nearby beach areas. In this investigation the authors estimated the number of gastrointestinal and respiratory illness episodes associated with the microbial contamination of coastal waters among bathers at Southern California beaches from 2000 through 2004. Bathers at the 67 beaches along the 350-km coastline of Southern California were the study population in this investigation. The authors' estimates were derived from a simulation model, which utilized water quality, beach attendance, and bathing-rate data, along with the three concentration-response relationships that underlie US Environmental Protection Agency, World Health Organization, and European Union marine water-quality guidelines. Given the absence of a general surveillance program to monitor these illnesses in Southern California, simulation modeling provides an established method to derive health risk estimates, despite additional analytic uncertainty that may accompany modeling-based analyses. An estimated 689,000 to 4,003,000 gastrointestinal illness episodes and 693,000 respiratory illness episodes occurred each year. The majority of illnesses (57% to 80%) occurred during the summer season as a result of large seasonal increases in beach attendance and bathing rates. As 71% of gastroenteritis episodes were estimated to occur when the water quality was considered safe for bathing, California's marine water-contact standards may be inadequate to protect the health of bathers.
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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 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".