Influence of Livestock Manure Type on Transport of Escherichia coli in Surface Runoff
Bibliographic record
Abstract
Abstract Since livestock manure type may influence transport of Escherichia coli (E. coli) in runoff, the choice of which type of livestock manure to apply to cropland may be a potential beneficial management practice (BMP) to reduce and manage E. coli in runoff. Four common manure types (beef, dairy, chicken, hog) were applied to a clay loam soil in small runoff boxes, and a rainfall simulator was used to generate artificial runoff. Runoff samples were collected at three successive time intervals (0 to 5, 5 to 15, 15 to 30 min) and analyzed for flow-weighted mean concentrations (FWMC) of E. coli as well as mass loss of E. coli expressed as a percentage of total E. coli applied. Manure treatment had a significant (p ≤ 0.10) influence on FWMC of E. coli in runoff. The FWMC of E. coli in runoff for the dairy (33.3 CFU per 100 mL) treatment was similar to the control (3.2 CFU per 100 mL), but E. coli concentrations for the beef (955 CFU per 100 mL), chicken (1,134 CFU per 100 mL), and hog (368 CFU per 100 mL) treatments were all significantly greater than the control. The FWMC values were not significantly different among the four manured treatments except for dairy versus chicken manure, where values were significantly lower for dairy manure. Concentrations of E. coli were less than the guideline for recreation waters (< 200 CFU per 100 mL) for the control and dairy treatment, but exceeded this guideline for beef, chicken, and hog manures, suggesting that dairy manure may be better than the other three manures for protecting surface water bodies for recreational use. Our study suggests that manure type may be a possible BMP to manage and control FWMC of E. coli in surface waters.
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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 | 0.000 | 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) | 0.001 | 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".