Microbial thiosulphate reaction arrays: the interactive roles of Fe(III), O<sub>2</sub> and microbial strain on disproportionation and oxidation pathways
Bibliographic record
Abstract
In this work, we experimentally evaluate pH and SO4(2-) dynamics associated with abiotic and microbial S2O3(2-) oxidation under varying [O2], [Fe(III)] and microbial strain/consortia (two pure strains, Acidithiobacillus ferrooxidans, Acidithiobacillus thiooxidans, their consortia, and two enrichments from an acidic environmental system, Moose Lake 2002 and Moose Lake 2003). Results of the batch experiments demonstrate highly active microbial processing of S2O3(2-) while abiotic controls under identical experimental conditions remain static with no pH decrease. When abiotic controls were manually titrated with acid to achieve similar pH decreases to those occurring in the microbial treatments, different S pathways were involved. In particular, disproportionation is a substantial component of initial microbial S2O3(2-) processing, and is accelerated by the presence of Fe(III), indicating that recycling of S through intermediate oxidation states is likely to be widespread in acidic mine environments where high [Fe(III)] is common. Furthermore, the microbially mediated S reaction pathways were dependent on both environmental conditions and microbial strain/consortia, indicating that microbial community structure also plays a key role. Collectively, these results highlight the importance of microbial activity, their poor representation by abiotic S models, the likelihood that Fe(III), rather than O2, is a key control on microbial S processing in acid environments and the need to identify the microbial community/strain involved.
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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 | 0.001 |
| 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 | 0.001 | 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 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".