EFFECT OF BIOAUGMENTATION ON MICROBIAL TRANSPORT, WATER INFILTRATION, MOISTURE LOSS, AND SURFACE HARDNESS IN PRISTINE AND CONTAMINATED SOILS
Bibliographic record
Abstract
Three different soils, a clay, a pristine sandy loam and a PCB-contaminated sandy loam, were bioaugmented to determine the influence of clay content and contaminants on the transport of bacteria in unsaturated soils, using surface irrigation water as a transport medium. The results indicate that the transport of the implanted bacteria was influenced negatively more by the presence of PCBs than by the clay content of the soil. Transport was directly related to the frequency of irrigation and length of the intervals between irrigation periods, making these variables important factors to consider when applying bioaugmentation via downward percolating water. Other parameters measured after bacterial bioaugmentation were water infiltration, moisture loss, and surface hardness of these soils. Surface water infiltration was affected more by the soil clay content than by the hydrophobic contaminant. Infiltration was significantly but differently influenced by bioaugmentation, positively in clay, negatively in sandy loam, and negatively (to a lesser extent) in the PCB-contaminated sandy loam soils. Moisture loss was slower in the bioaugmented soil than in the control soils, with this difference being most pronounced in the PCB soil. High moisture loss in the bioaugmented clay soil rendered it the hardest soil for surface penetration.
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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".