Diurnal variability in concentrations and sources of <i>Escherichia coli</i> in three streams
Bibliographic record
Abstract
Microbial contamination is a major concern for drinking water worldwide. Many monitoring protocols that use one or very few samples are inadequate and introduce a very large margin of error. An intensive sampling program needs to be conducted to characterize the Escherichia coli concentrations of a source water stream prior to establishing a monitoring program so that the sample frequency can be determined statistically, based on an acceptable margin of error. Developing meaningful monitoring programs for managing bacterial water quality is dependant on scientific data that determine the bacterial sources. In this study, three streams from drinking water watersheds were sampled every 15 min over a 24 h period on three different days to determine the concentrations of E. coli and to identify their sources, using ribosomal RNA finger printing (ribotyping). The concentrations of E. coli varied throughout the day in each of the three streams. Ribotyping identified many different animal sources of E. coli in the samples. The sources of E. coli varied significantly with stream (P < 0.001, df = 16). The development of monitoring programs for watersheds needs to consider the watershed, and care needs to be taken in selecting appropriate sample sites, sampling regime, and number of samples taken during each sampling period. This note provides a prescription for the development of monitoring programs for watersheds.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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 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".