Spatial and Annual Variability in Concentrations and Sources of <i>Escherichia coli</i> in Multiple Watersheds
Bibliographic record
Abstract
Nonpoint source fecal contamination is a concern for drinking water supplies worldwide. In this study, 4812 E. coli isolates were classified to source. Results of this experiment show that the fecal coliform (FC) counts varied by year, month, and site, for each of the watersheds sampled. For both years, the lowest FC counts tended to be at the highest elevation sites followed by the drinking water intake sites at the lowest elevation. The highest FC counts tended to be at the mid-elevation sites on BX, Deer, and Duteau Creeks. The sources of E. coli varied significantly with stream for 2003 and 2004 (P < 0.001, df = 39), although the main sources of E. coli (avian, deer/elk, canine, rodent, bovine, and bear) tended to be similar between watersheds. The dominant sources of E. coli changed from 2003 (avian, deer/elk, and canine) to 2004 (avian, bovine, and rodent). It is important to look at the results of more than 1 year of source tracking data to get a better picture of the dominant sources within a watershed. Overall, wildlife was the largest contributor of E. coli to the watersheds in both 2003 (> 84%) and 2004 (> 73%).
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".