A physical descriptive model for predicting bacteria level variation at a dynamic beach
Bibliographic record
Abstract
A rational-based physical descriptive model (PDM) has been developed to predict the levels of Escherichia coli in water at a beach with dynamic conditions in the Greater Toronto Area (GTA), Ontario, Canada. Bacteria loadings in the water were affected not only by multiple physical factors (precipitation, discharge, wind, etc.), but also by cumulative effects, intensity, duration and timing of storm events. These may not be linearly related to the observed variations in bacteria levels, and are unlikely to be properly represented by a widely used multiple linear regression model. In order to account for these complex relationships, the amounts of precipitation and nearby creek discharge, the impact of various time-related factors, lag time between events and sample collection, and threshold for different parameters were used in determining bacteria levels. This new comprehensive PDM approach improved the accuracy of the E. coli level predictions in the studied beach water compared to the previously developed statistical predictive and presently used geometric mean models. In spite of the complexity and dynamic conditions at the studied beach, the PDM achieved 75% accuracy overall for the five case years examined.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".