Pathogen and indicator variability in a heavily impacted watershed
Bibliographic record
Abstract
Water samples were collected from 36 locations within the Grand River Watershed, in Southwestern Ontario, Canada from July 2002 to December 2003 and were analyzed for total coliforms, fecal coliforms, Escherichia coli, Escherichia coli O157:H7, and thermophilic Campylobacter spp. A subset of samples was also analyzed for Cryptosporidium spp., Giardia spp., culturable human enteric viruses, and Clostridium perfringens. Storm and snowmelt events were sampled at two locations including a drinking water intake. For the majority of the events, the Spearman rank correlation test showed a positive correlation between E. coli levels and turbidity. Peaks in pathogen numbers frequently preceded the peaks in numbers of indicator organisms and turbidity. Pathogen levels sometimes decreased to undetectable levels during an event. As pathogen peaks did not correspond to turbidity and indicator peaks, the correlations were weak. Weak correlations may be the result of differences in the sources of the pathogens, rather than differences in pathogen movement through the environment. Results from this investigation have implications for planning monitoring programs for water quality and for the development of pathogen fate and transport models to be used for source water risk assessment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".