Bioremediation of Residual Fertilizer Nitrate
Bibliographic record
Abstract
The prospect of using wastewater containing high loads of soluble organic matter (OM) for removing residual agricultural chemicals (fertilizer, pesticide, or herbicide) in farm soil, although promising, could have adverse effects on soil agricultural quality as a result of development of redoximorphic features in the soil profile. In this study, the effect of organic carbon supplement for bioremediation of residual fertilizer nitrate on soil properties, redox potential (Eh), pH, and metal ion mobilization was studied using sandy soils packed in columns. The study was included in a general project, described elsewhere (Ugwuegbu et al., 2000), undertaken to evaluate use of controlled water table management (WTM) systems to supply organic carbon for creating a reduced environment conducive to denitrification of residual fertilizer nitrate leaching from the farm to subsurface water. The columns were subjected to subirrigation with water containing soluble organic carbon in the form of glucose. The work was carried out in two experimental setups and the long-term effect of a range of glucose concentrations on the Eh, pH, and soluble levels of Fe and Mn was investigated. From the results obtained, it could be concluded that excessive organic carbon supplement to soil can have adverse effects on soil quality and that Eh and soluble Fe are the two most practical parameters for monitoring soil health during treatment of farm chemicals.
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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".