Integrative analysis of <i>Geobacter</i> spp. and sulfate-reducing bacteria during uranium bioremediation
Bibliographic record
Abstract
Abstract. Enhancing microbial U(VI) reduction with the addition of organic electron donors is a promising strategy for immobilizing uranium in contaminated groundwaters, but has yet to be optimized because of a poor understanding of the factors controlling the growth of various microbial communities during bioremediation. In previous field trials in which acetate was added to the subsurface, there were two distinct phases: an initial phase in which acetate-oxidizing, U(VI)-reducing Geobacter predominated and U(VI) was effectively reduced and a second phase in which acetate-oxidizing sulfate reducing bacteria (SRB) predominated and U(VI) reduction was poor. The interaction of Geobacter and SRB was investigated both in sediment incubations that mimicked in situ bioremediation and with in silico metabolic modeling. In sediment incubations, Geobacter grew quickly but then declined in numbers as the microbially reducible Fe(III) was depleted whereas the SRB grow more slowly and reached dominance after 30–40 days. Modeling predicted a similar outcome. Additional modeling in which the relative initial percentages of the Geobacter and SRB were varied indicated that there was little to no competitive interaction between Geobacter and SRB when acetate was abundant. Further simulations suggested that the addition of Fe(III) would revive the Geobacter, but have little to no effect on the SRB. This result was confirmed experimentally. The results demonstrate that it is possible to predict the impact of amendments on important components of the subsurface microbial community during groundwater bioremediation. The finding that Fe(III) availability, rather than competition with SRB, is the key factor limiting the activity of Geobacter during in situ uranium bioremediation will aid in the design of improved uranium bioremediation strategies.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".