Low water activity limits bentonite-associated microbial growth
Bibliographic record
Abstract
AIMS: This research investigated the impact of water activity on microbial abundance estimates from bentonite under conditions relevant to a deep geological repository for used nuclear fuel. Because previous research tested saturation of bentonite within pressurized vessels, the goal of this study was to assess how water activity alone, without pressure, prevents increases in microbial abundance estimates from bentonite samples. METHODS AND RESULTS: Small-scale microcosms of bentonite were hydrated to low, medium, or high water activities, with Type I water, reference groundwater, or bacterial growth medium, then incubated under oxic or anoxic conditions. At six timepoints over a 6-month period, microorganisms were quantified using cultivation-dependent and independent approaches, and 16S rRNA genes were sequenced to monitor relative abundance changes of microbial taxa. Large-scale incubations were then conducted to also enable analyses of phospholipid fatty acids and natural organic matter. The results demonstrate that increasing water activity was associated with higher microbial abundance estimates for oxic condition incubations, with water-activity-dependent actinobacterial growth. In contrast, no significant microbial abundance changes were observed for anoxic microcosm incubations. For all tested conditions, we did not detect any increases in sulfate-reducing bacteria abundance estimates. CONCLUSIONS: Although low water activity conditions prevented changes in microbial abundances for bentonite samples incubated under oxic conditions, anoxic conditions alone prevented increases in abundances of culturable microorganisms. These results complement previous pressure-vessel studies that have shown how low water activity and elevated pressure simultaneously reduce the abundance of viable microorganisms that can initially proliferate during the saturation process.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".