Bacterial contamination of tile drainage water and shallow groundwater under different application methods of liquid swine manure
Bibliographic record
Abstract
A 2 year field experiment evaluated liquid manure application methods on the movement of manure-borne pathogens (Salmonella sp.) and indicator bacteria (Escherichia coli and Clostridium perfringens) to subsurface water. A combination of application methods including surface application, pre-application tillage, and post-application incorporation were applied in a randomized complete block design on an instrumented field site in spring 2007 and 2008. Tile and shallow groundwater were sampled immediately after manure application and after rainfall events. Bacterial enumeration from water samples showed that the surface-applied manure resulted in the highest concentration of E. coli in tile drainage water. Pre-tillage significantly (p < 0.05) reduced the movement of manure-based E. coli and C. perfringens to tile water and to shallow groundwater within 3 days after manure application (DAM) in 2008 and within 10 DAM in 2007. Pre-tillage also decreased the occurrence of Salmonella sp. in tile water samples. Indicator bacteria and pathogens reached nondetectable levels within 50 DAM. The results suggest that tillage before application of liquid swine manure can minimize the movement of bacteria to tile and groundwater, but is effective only for the drainage events immediately after manure application or initial rainfall-associated drainage flows. Furthermore, the study highlights the strong association between bacterial concentrations in subsurface waters and rainfall timing and volume after manure application.
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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) | 0.001 | 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".