Performance of Mixed Organic Substrates during Treatment of Acidic and Moderate Mine Drainage in Column Bioreactors
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it