Fecal Indicator Bacteria Variability in Samples Pumped from Monitoring Wells
Bibliographic record
Abstract
The detection of microbiological contamination in drinking water from groundwater wells is often made with a limited number of samples that are collected using traditional geochemical sampling protocols. The objective of this study is to examine the variability of fecal indicator bacteria, as observed using discrete samples, due to pumping. Two wells were instrumented as multilevel piezometers in a bedrock aquifer, and bacterial enumeration was conducted on a total of 166 samples (for total coliform, fecal coliform, Escherichia coli, and fecal streptococci) using standard membrane filtration methods. Five tests were conducted using pumping rates ranging from 0.3 to 17 L/min in a variety of purging scenarios, which included constant and variable (incremental increase and decrease) flow. The results clearly show a rapid and reproducible, 1 to 2 log-unit decrease in fecal indicator bacteria at the onset of pumping to stabilized, low-level concentrations prior to the removal of three to five well volumes. The pumping rate was not found to be correlated with the magnitude of observed bacterial counts. Based on the results, we suggest sampling protocols for fecal indicator bacteria that include multiple collections during the course of pumping, including early-time samples, and consider other techniques such as microscopic enumeration when assessing the source of bacteria from the well-aquifer system.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".