Modeling coliforms in storm water plumes
Bibliographic record
Abstract
The recreational waters near many large cities in the United States and Canada are severely impaired by pathogens that are present in the storm water runoff. In separated sewers the pathogen sources may be cross-flows between the sanitary and storm water systems. This paper presents the methodology that was used in developing a forecasting model for pathogen indicators for recreational sites in the receiving waters of multiple storm water outfalls. The objective of the model is to give a timelier indicator of beach water quality than conventional beach monitoring, which takes about 2 d for laboratory results. The model used for the study was based on the Princeton Ocean Model. The forecasting system consists of nested hydrodynamic models and a bacteria fate–transport submodel. Calibration and validation is based on 6 years of field studies, laboratory analyses, and experiments. The methodology is illustrated by a case study of the impact of storm water flows on the south shore of Lake Pontchartrain, Louisiana, which has been banned for swimming since 1985. The water quality data included: pathogen indicators (fecal coliform, Enterococci, and E. Coli), water chemistry parameters, turbidity, and nutrients. Key words: modeling, water quality, pathogens, fecal coliform, stormwater runoff.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".