Performance of sulphate- and selenium-reducing biochemical reactors using different ratios of labile to recalcitrant organic materials
Bibliographic record
Abstract
Successful operation of sulphate-reducing bioreactors using complex organic materials depends on providing a balance between more easily degrading material that achieves reasonable kinetics and low hydraulic retention times, and more slowly decomposing material that sustains performance in the long term. In this study, two organic mixtures containing the same ingredients typical of bioreactors used at mine sites (woodchips, hay and cow manure) but with different ratios of wood (recalcitrant) to hay (labile) were tested in six continuous flow bioreactors treating synthetic mine-affected water containing 600 mg/L of sulphate and 15 μg/L of selenium. The reactors were operated for short (5-6 months) and long (435-450 days) periods of time at the same hydraulic retention time of 15 days. There were no differences in the performance of the bioreactors in terms of sulphate-reduction over the short term, but the wood-rich bioreactors experienced variable and sometimes unreliable sulphate-reduction over the long term. Presence of more hay in the organic mixture was able to better sustain reliable performance. Production of dissolved organic compounds due to biodegradation within the bioreactors was detected for the first 175-230 days, after which their depletion coincided with a crash phase observed in the wood-rich bioreactors only.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".