Fate and Transport Modeling of Potential Pathogens: The Contribution From Sediments<sup>1</sup>
Bibliographic record
Abstract
Abstract: Escherichia coli was used as a bacterial tracer for the development, calibration, and validation of a watershed scale fate and transport model to be extended to a suite of reference pathogens ( Cryptosporidium , Giardia , Campylobacter , E. coli O157:H7). E. coli densities in water and sediments from the Blackstone River Watershed, Massachusetts, were measured at three sites for a total of five wet weather events and three dry weather events covering three seasons. The confirmed E. coli strains were identified by ribotyping for tracking the sources of E. coli and for determining the association of downstream E. coli isolates with isolates from upstream sediments. A large number of downstream samples were associated with upstream sediment sources of E. coli . E. coli densities ranged from 71 to 6,401 MPN/100 ml in water samples and from 2 to 335 MPN/g in sediments. Pearson correlation analysis revealed significant correlations between E. coli and total coliforms in water ( r = 0.777, p < 0.01) and sediments ( r = 0.728, p < 0.01). In addition, E. coli concentrations in water were weakly correlated with sediment particle size and sediment concentrations ( r = 0.298, p < 0.01). A hydrologic model, WATFLOOD/SPL9, was used to predict the temporal and spatial variation of E. coli in the Blackstone River. The rapid rise of stream E. coli densities was more accurately predicted by the model with the inclusion of sediment resuspension, thus demonstrating the importance of the process.
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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".