Bioremediation of Chlorate and Chromium in Soil Columns Using Contaminated Site Native Culture
Bibliographic record
Abstract
Abstract Chlorate and hexavalent chromium (chromate) are both widely used in different industries, and the improper waste management in the past left many sites with elevated concentrations in groundwater that pose potential risk to human and/or ecological health. Bioremediation is a sustainable management solution that can reduce both of these contaminants to less toxic species. In our earlier microcosms experiments, we have demonstrated that native microorganisms collected from a site contaminated with chlorate and chromate can lower the concentration of these chemicals in groundwater to acceptable regulatory levels provided sufficient electron donor, nitrogen, and phosphorous are provided. In this study, continuous flow column experiments were performed using soil from the site impacted by both chlorate and chromate in the Province of Manitoba (Canada) and synthetic groundwater amended with acetate, nitrogen, and phosphorous. The objective was to evaluate at a bench scale possibility of in‐situ groundwater treatment. Concentrations of chromate and chlorate measured in the columns' effluent water dropped by 86% and 96%, respectively. However, increased biomass and precipitation of trivalent chromium reduced the water flow rate in the columns, a concern for implementing this method as a long‐term in‐situ remediation solution.
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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) | 0.001 | 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 | 0.001 | 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".