Effects of solid manure particle fractionation on transport, retention, and release of Escherichia coli
Bibliographic record
Abstract
Understanding the effect of manure particle fractionation on transport, retention, and release of bacteria plays a critical role in manure management and environmental policies that address soil and water bacterial pollution. Compared to soil particle size, there is less understanding of the importance of solid manure particle size and fractionation on bacterial fate and transport in soils. Four different cow manure particle sizes (0.25, 0.5, 1, and 2 mm) were used to investigate Escherichia coli fate in a saturated loamy sand soil. Leaching experiments were performed for up to 20 pore volumes. Preferential transport of chloride mitigated as manure particle size increased. The larger manure fractions (1 and 2 mm) showed greater heterogeneity in bacteria transport and release; smaller manure fractions (0.25 and 0.5 mm) had a greater bacteria retention with retarded release. Bacteria release was associated with transport and re-entrainment of manure particles through soil columns. The results highlighted the contribution of fine and transported particles as of primary importance for retention near the surface and transporting bacteria in soil. Similar retention shapes (i.e., exponential) for different fractions illustrated the similarity of manure source, where greater retention was observed at 0−3 cm depth for the smallest (0.25 mm) and largest (2 mm) manure fractions. The findings also highlighted the dependency of bacteria transport, retention, and release on manure physical fractionation, which should be considered in managing soil and manure practices in the field.
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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) | 0.000 | 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".