ASSESSING LEVELS OF PATHOGENIC CONTAMINATION IN A HEAVILY IMPACTED RIVER USED AS A DRINKING-WATER SOURCE
Bibliographic record
Abstract
This paper describes initial results from a research program that aims to gain greater understanding of sources of pathogens and the environmental factors that influence their survival and transport in watersheds. An additional goal is to enhance the ability to predict potential levels of pathogenic microorganisms arriving at drinking-water treatment plant intakes. The objectives will be supported by an intensive monitoring program examining the temporal and spatial variability of pathogens in a test watershed (the Grand River Watershed, Ontario). As many as 500,000 people potentially receive at least part of their drinking water from the Grand River. The watershed has significant urban and agricultural use. Sampling for total coliforms, fecal coliforms, Escherichia coli, Escherichia coli O157:H7, and Campylobacter spp. began in July 2002. Although presumptive tests were occasionally positive, no Escherichia coli O157:H7 or Campylobacter spp. were confirmed to be present in water samples taken. Escherichia coli O157:H7 was, however, detected in a tributary of the Grand River during an initial investigation. Preliminary results did not show any statistically significant differences between coliform concentrations upstream and downstream of wastewater treatment plants. Data suggest that nonpoint sources may have a greater effect on routine stream coliform concentrations.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".