Sampling <i>Escherichia coli</i> and Total Coliforms using Stainless Steel Suction Lysimeters
Bibliographic record
Abstract
Methods for monitoring fecal indicator bacteria in soil pore water are needed to improve characterization of bacterial fate and transport in the vadose zone. Laboratory experiments were conducted using commercially available stainless steel suction lysimeters to assess their capability for sampling total coliform (TC) and Escherichia coli (EC) indicator bacteria. The lysimeters were placed in a liquid‐filled tank containing either wastewater effluent or bacteria‐free solution. During initial sampling, concentrations for both TC and EC were reduced by up to 3‐log (i.e., 1000‐fold) compared to wastewater input concentrations. Bacterial retention rates in the lysimeters decreased to values of approximately 0.5–1.5 log with repeated sampling and during subsequent sampling events. After placing the samplers in essentially bacteria free water, continued detections of TC and EC suggested a memory effect, but concentrations generally returned to within 1.0‐log of the input concentration after two or three samples were collected. The results indicate that stainless steel lysimeters can provide a reliable method for semi‐quantitative enumeration of fecal bacteria indicators in soil pore water.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".