Human health risk-based design of ocean outfalls
Bibliographic record
Abstract
In recent years there has been a trend towards specifying water quality guidelines from an epidemiological viewpoint. This raises the possibility that ocean outfall design could itself be looked at from the point of view of the risk to human health from exposure to specific pollutants. An approach to health risk-based design of ocean outfalls is presented in this paper. The approach is based on an integration of the principles of human health risk assessment and ocean outfall design. Hazard identification of rota-virus—used as an example of a typical pathogen in sewage—and dose–response relationships are reviewed. An empirical initial dilution model is combined with secondary dilution and decay models to quantify virus exposures during swimming. The health risk to a swimmer at a specified distance from the outfall discharge is then evaluated by integrating the exposure and dose–response models. Risk of infection, clinical illness, and mortality are all evaluated with a case study using data from an existing outfall. The applicability of the approach to ocean outfall design is presented by quantifying the risks of using a public beach for swimming for a given outfall design scenario. Different scenarios are used to simulate the risk of infection, clinical illness, and mortality, from which the location of the designed outfall may be determined to better assure the safety of swimming areas. A probabilistic analysis is presented to investigate the uncertainty in estimated health risk from potential design scenarios.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".