Soil wetting state and preferential transport of <i>Escherichia coli</i> in clay soils
Bibliographic record
Abstract
Transport of Escherichia coli (E. coli) through soil to drinking and recreational water may pose a serious health risk. The objective of this study was to determine how initial preferred soil wetting state influences the preferential transport of E. coli in a clay soil. A strain of E. coli marked with green fluorescent protein (gfp) was gravity-fed-sprinkler-applied as simulated rainfall to three replicates in different wetting states, along with Cl- and adsorptive dye near Plenty, SK. Canada. After 48 h, a 50 × 50 × 50 cm 3 block was excavated to determine the transport pathways. Digital image analysis of horizontal sections provided estimates of dye coverage. Escherichia coli were significantly filtered in the top 10 cm of soil with concentration profiles similar to that of Cl-. Ratios of E. coli to Cl- did not show significant differences among treatments (P < 0.05) and indicated that below 10 cm depth, E. coli and Cl- were preferentially transported along the same pathways with no significant difference between plots. Results show that the majority of E. coli and Cl- were filtered when transported through the discontinuous pores of the near-surface matrix and suggest a saturated layer that controlled infiltration into organized root channels, resulting in preferential flow. Key words: Preferential flow, Escherichia coli, wetting state, Vertisol, conducting areas
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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 | 0.000 |
| 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".