Tolerance of Chemoorganotrophic Bioleaching Microorganisms to Heavy Metal and Alkaline Stresses
Bibliographic record
Abstract
The bioleaching potential of the bacterium Bacillus mucilaginosus and the fungus Aspergillus niger towards industrial residues was investigated by assessing their response towards various heavy metals (including arsenic, cadmium, cobalt, chromium, nickel, lead, and zinc) and elevated pH. The plate diffusion method was performed for each metal to determine the toxicity effect. Liquid batch cultures were set up for more quantitative evaluation as well as for studying the influence of basicity. Growth curves were prepared using bacterial/fungal growth counting techniques such as plate counting, optical density measurement, and dry biomass determination. Cadmium, nickel, and arsenite had a negative influence on the growth of B. mucilaginosus, whereas A. niger was sensitive to cadmium and arsenate. However, it was shown that growth recovered when microorganisms cultured in the presence of these metals were inoculated onto metal-free medium. Based on the findings of the bacteriostatic/fungistatic effect of the metals and the adaptability of the microorganisms to fairly elevated pH values, it is concluded that both strains have potential applicability for further research concerning bioleaching of alkaline waste materials.
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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".