Seeing is Predicting: Water Clarity-Based Nowcast Models for E. coli Prediction in Surface Water
Bibliographic record
Abstract
Given the 24–48 h turn-around time of conventional surveillance approaches, methods are needed that improve the timeliness and accuracy of recreational water quality risk assessments. Although one useful approach is to combine existing monitoring programmes with predictive faecal indicator bacteria (FIB) models, these models are largely ‘top-down’ in their approach to safeguarding public health. Beyond being simply ‘advised when to avoid swimming’, there is an increasing awareness amongst the general public regarding the role they can play in water quality monitoring. Using quantile, maximum value and optimized incremental modelling approaches, this study reports on the possibility of developing intuitive, public-friendly models that are based on the physical appearance of water (clarity), to estimate 8103 nation-wide E. coli concentrations in rivers, and to assess whether water is safe to swim in. If swimmers were to avoid river waters with <1.1 m black disc visibility during autumn and summer, and river waters with values <0.5 m black disc visibility during spring and winter, they would also avoid microbial hazards that are associated with exceedances of the 540 CFU/100 mL single sample bathing water standard. Regardless of the climatic season, stream order classification, catchment land cover or geology of streams considered, the clarity-based E. coli models performed well as they presented with sensitivity, specificity and accuracy values of at least 72%. The developed models offer the benefit of providing a faster method for estimating E. coli concentration, potentially engaging the public in water monitoring, and allowing them to make informed decisions on whether it is safe to swim.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".