Functional redundancy imparts process stability to acidic Fe( <scp>II</scp> )‐oxidizing microbial reactors
Bibliographic record
Abstract
In previous work, lab-scale reactors designed to study microbial Fe(II) oxidation rates at low pH were found to have stable rates under a wide range of pH and Fe(II) concentrations. Since the stirred reactor environment eliminates many of the temporal and spatial variations that promote high diversity among microbial populations in nature, we were surprised that the reactors supported multiple taxa presumed to be autotrophic Fe(II) oxidizers based on their phylogeny. Metagenomic analyses of the reactor communities revealed differences in the metabolic potential of these taxa with respect to Fe(II) oxidation and carbon fixation pathways, acquisition of potentially growth-limiting substrates and the ability to form biofilms. Our findings support the hypothesis that the long-term co-existence of multiple autotrophic Fe(II)-oxidizing populations in the reactors are due to distinct metabolic potential that supports differential growth in response to limiting resources such as nitrogen, phosphorus and oxygen. Our data also highlight the role of biofilms in creating spatially distinct geochemical niches that enable the co-existence of multiple taxa that occupy the same apparent metabolic niche when the system is viewed in bulk. The distribution of key metabolic functions across different co-existing taxa supported functional redundancy and imparted process stability to these reactors.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".