Use of viral indicators to assess public health risk to shellfish growing areas: A case study from Blaine, Washington
Bibliographic record
Abstract
A hydrographic dye study of effluent from the Lighthouse Point Water Reclamation Facility in Blaine, Washington, was conducted in November 2012. Six cages filled with oysters were deployed at various locations (stations) along the anticipated path of the effluent to correlate the dye concentrations found at the cages with the indicator bacteria and viral findings in the oysters. Sampling was also conducted at the plant to assess bacteria and virus removal efficiencies through the treatment process. The study objectives were to: (1) determine the bacterial and viral conditions in the influent and effluent and removal efficiencies for a WWTP using membrane filtration (2) determine the bacterial and viral conditions that could arise in receiving waters under a short term lapse in treatment at the WWTP; (3) provide guidance to the Washington Department of Health (WA DOH) regarding the WWTP closure zone based on dilution of effluent (4) research the dilution level needed to achieve reduction in viruses to ensure the safety of shellfish harvested near WWTPs as part of FDA’s dilution guidance The proposed presentation addresses the session theme based on the following features: (1) A description of tools and methodology currently used by FDA to assess risk from wastewater outfalls to commercial shellfish growing areas, including development of an GIS application for mapping dye plumes in real time, (2) Current efforts by FDA to develop an easily quantifiable viral indicator (MS2 Coliphage) and how this indicator correlates with presence of viral pathogens such as Adenovirus and Norovirus in oysters, (3) Evaluation fecal coliform bacteria indicator to assess public health risk from viral pathogens with wastewater plants employing membrane filtration treatment, and (4) Since Blaine sits on the border between the US and Canada, the case study also highlights transborder pollution issues.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".