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.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".