Determining Sources of Fecal Pollution in Water for a Rural Virginia Community
Bibliographic record
Abstract
This project involves developing and applying bacterial source tracking (BST) methodology to determine sources of fecal pollution in water for a rural community (Millwood, VA). Antibiotic resistance analysis (ARA) is the primary BST method for fecal source identification, followed by randomly amplified polymorphic DNA (RAPD) analysis for confirmation. Millwood consists of 66 homes, all served by individual septic systems, and a stream (Spout Run) passes through the center of the community. Spout Run drains a 5,800 ha karst topography watershed that includes large populations of livestock and wildlife. Stream and well samples were collected monthly and analyzed for fecal coliforms and fecal streptococci, starting in 5/99 and ending in 5/00. Twelve percent of the well samples and 92% of the stream samples were positive for fecal coliforms, and 26% of the stream samples exceeded the recreational water standard (1,000 fecal coliforms/100 ml). ARA of fecal streptococci recovered from the stream samples indicated that isolates of human origin appeared throughout the stream as the stream passed through Millwood with a yearly average of (approx. 10% human, 30% wildlife, and 63% livestock), and the percent human origin isolates declined downstream from Millwood. These results were obtained by comparing the antibiotic resistance profiles of stream isolates against a library of 1,174 known source isolates with correct classification rates of 94.6% for human isolates, 93.7% for livestock isolates, and 87.8% for wildlife isolates. There is a human signature in Spout Run, but it is small compared to the proportion of isolates from livestock and wildlife. The sporadic instances where well water samples were positive appeared primarily during very dry periods. Restricting livestock access to streams can dramatically lower fecal coliform counts during the unusually hot and dry periods. Reducing FC counts to below recreational water standards for Virginia (1000 per 100ml for any one sample) may be achievable, however to maintain streams below standards may prove to be difficult, as Spout Run is in an area where there are large populations of Canada geese, deer, and other wildlife, and will be hard to restrict these animals.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".