Bibliographic record
Abstract
A process of using the manganese-oxidizing bacterium, Rhizobium etli, for the uptake of several metal ions present in acid mine drainage waters (AMD) such as Mn 2 +,ions as een mvesttgate .In t e actena manganese oxidation process previously developed in this laboratory (Moy, 1998), it was shown that Rhizobium etli was able to rapidly take up manganese in glucose media.Variation in media composition by increasing glucose concentration had marked effects on manganese uptake and its uptake rate.The addition of excess glucose promoted the production of extracellular polysaccharides (EPS) and subsequently enhanced manganese uptake.The maximum uptake of manganese occurred at the maximum EPS concentration in medium, that was about 1560 mg/L of manganese or 3 8% of cellular dry weight with the average uptake rate of 39 mg/L/h, which occurred in the presence of 7.31 g/L EPS resulting from medium with glucose concentration of 25.8 g/L (C/N=l 0).Characteristics of manganese uptake by Rhizobium et/i indicated active metabolic uptake and sensitive to other metal cations since the presence of Zn 2 +, F e 2 +, Mg 2 + (in high concentration), Cu 2 + and Pb 2 + ions reduced the manganese uptake and uptake rate.The maximal metal uptake in binary mixtures of manganese and these metals was about 1000 mg/L of Mn, 180 mg/L of Zn, 50 mg/L of Mg and 30 mg/L of Fe but the bacterium failed to take up Cu and Pb.The presence of multi metal ions reduced significantly the manganese uptake rates from 12-34 mg/L/h in binary metal mixtures to 5.7 mg/L/h in multi metal mixture, indicating that the presence of multi metal ions resulted in some cumulative inhibitory effect on the manganese uptake rate.With the given initial metal concentrations that mimic the metal composition in a typical AMD (255 mg/L Mn, 20 mg/L Zn, 30 mg/L Fe, 20 mg/L Mg, 40 mg/L Cu and 0.5 mg/L Pb), Rhizobium etli was capable of removing Mn, Zn, Fe and Mg to levels below discharge standard, supporting the idea that manganese-oxidizing bacteria could potentially participate in AMD mitigation.This process has practical potential for removing metals from AMD, particularly for AMD that contains low levels of Pb and Cu.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".