Performance of Mixed Organic Substrates during Treatment of Acidic and Moderate Mine Drainage in Column Bioreactors
Bibliographic record
Abstract
Mushroom compost, wood chips, sawdust, cow manure, and rice straw were characterized and tested in three combinations as prospective substrates during the treatment of acidic (pH 3) and moderate (pH 6) mine drainage in 3.5 L column bioreactors operated for 167 days, at 3 days of hydraulic retention time. Mixtures gave comparable performances in each pH condition with satisfactory efficiencies. After less than a 2-week acclimation period, bacteria became active, as indicated by a pH increase and sulfide production. Dissolved organic carbon (DOC) consumption was higher in acidic condition, whereas sulfate removal mainly occurred in the early reaction period. There were significant differences in the sulfate and DOC results from acidic relative to moderate mine drainage columns. Aluminum was readily removed (nearly 100%) by all the reactors. Iron removal was better for acidic (98–99%) than for moderate (73–85%) mine drainage. Manganese, mostly leached out from substrate materials, prevailed in early reaction times, followed by a steady decrease toward the end. Results demonstrate the potential utility of mixed substrates for enhancing the performances of bioreactors for mine drainage treatment. However, longer lasting times of DOC would characterize the moderate mine drainage condition.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".