Experimental study of iron and silica immobilization by bacteria in mixed Fe-Si systems: implications for microbial silicification in hot springs
Bibliographic record
Abstract
The immobilization of silica and iron by the bacteria Bacillus subtilis was monitored in controlled microcosms to elucidate the role iron may play in aiding bacterial silicification in hot springs. Silica and iron immobilization was monitored as a function of bacterial concentration, iron concentration, and silica concentration (both undersaturated and oversaturated with respect to amorphous silica). Results demonstrate that bacterial cells do immobilize more Fe than bacteria-free systems in solutions with iron concentrations [Formula: see text]50 ppm Fe. However, as iron concentrations increase, the difference between Fe immobilization in bacterial and bacteria-free systems decreases as non-bacterially mediated precipitation processes dominate. Additionally, bacterial systems that had immobilized more Fe compared with bacteria-free systems did not immobilize more silica than bacteria-free systems. By comparing molar ratios of (silica in solution)/(bacterially bound Fe), it is evident that insufficient iron is bound to the bacterial surface to act as an effective salt bridge for silica sorption. This appears to be because much of the iron is immobilized by non-bacterially mediated precipitation of phases such as Fe(OH) 3 and poorly ordered hydrous iron silicates. It follows that in silica-enriched hot springs, silica and iron immobilization processes are significantly dominated by non-bacterially mediated precipitation. Any bacterially mediated processes are exceedingly small and outside the resolution of these experiments.
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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 | 0.000 | 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".