Biofiltration of Residual Fertilizer Nitrate and Atrazine by <i>Rhizobium meliloti</i> in Saturated and Unsaturated Sterile Soil Columns
Bibliographic record
Abstract
This study was undertaken to investigate whether microbial bioaugmentation of subsurface soil with subsurface irrigation could be used as a biofiltration/biocontrol technology for agricultural pollutants. Nine Plexiglas columns, 458 mm long x 139 mm in diameter, were packed with a sterilized sandy loam soil. Subsurface irrigation, through a controlled water table management system, was used to deliver bacteria, Rhizobium meliloti A-025, to the soil and to maintain aerobic (unsaturated) or anaerobic (saturated) conditions in the columns. Nitrate and atrazine, a fertilizer and a corn herbicide, were applied to the soil surface, and leaching was affected by simulated rainfall events. The soil and drainage waters were analyzed for nitrate and atrazine residues after each rainfall simulation throughout the experimental period during which the soil was kept saturated for a total of 80 days and unsaturated for a total of 70 days. The monitoring of transport and survival of the implanted bacterial strain (A-025) showed that subsurface irrigation was successful in introducing and transporting the bacteria throughout the soil columns. During the saturated period, significantly more (95% probability) nitrate-N leached into the drainage waters from the control columns than from the bioaugmented columns; the increase being 450% or more for the abiotic control columns. The amount of atrazine that leached into the drainage waters during the unsaturated period was also significantly more from control columns as opposed to bioaugmented columns, with the increase being 262%.
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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 | 0.000 | 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".