Decline in Performance of Biochemical Reactors for Sulphate Removal from Mine-Influenced Water is Accompanied by Changes in Organic Matter Characteristics and Microbial Population Composition
Bibliographic record
Abstract
Successful long-term bioremediation of mining-influenced water using complex organic matter and naturally-occurring microorganisms in sub-surface flow constructed wetlands requires a balance between easily and more slowly degrading material. This can be achieved by combining different types of organic materials. To provide guidance on what mixture combinations to use, information is needed on how the ratio of labile to recalcitrant components affects the degradation rate and the types of microbial populations supported. To investigate this, different ratios of wood and hay were used in up-flow column bioreactors treating selenium- and sulphate-containing synthetic mine-influenced water. The degradation rates of crude fibre components appeared to be similar regardless of the relative amounts of wood and hay. However, the nature of the degradation products might have differed in that those produced in the hay-rich bioreactors were more biodegradable and supported high sulphate-reduction rates. Microorganisms in the sulphate-reducing and cellulose-degrading inocula persisted in the bioreactors indicating that bio-augmentation was effective. There was a shift in microbial community composition over time suggesting that different microbial groups were involved in decomposition of more recalcitrant material. When dissolved organic carbon (DOC) was over-supplied, the relative abundance of sulphate-reducers was low even through high sulphate-reduction rates were achieved. As DOC diminished, sulphate-reducers become more prevalent and their relative abundance correlated with sulphate concentrations rather than sulphate-reduction rate.
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How this classification was reachedexpand
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) | 0.001 | 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 teacher head, 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".