The effect of gram-positive (<i>Desulfosporosinus orientis</i>) and gram-negative (<i>Desulfovibrio desulfuricans</i>) sulfate-reducing bacteria on iron sulfide mineral precipitation
Bibliographic record
Abstract
Growth of two dissimilatory sulfate-reducing bacteria, Desulfosporosinus orientis (gram-positive) and Desulfovibrio desulfuricans (gram-negative), in a chemically defined culture medium resulted in similar growth rates (doubling times for each culture = 2.8 h) and comparable rates of H2S generation (D. orientis = 0.19 nmol/L S2– per cell per h; D. desulfuricans = 0.12 nmol/L S2– per cell per h). Transmission electron microscopy of whole mounts and thin sections revealed that the iron sulfide mineral precipitates produced by the two cultures were morphologically different. The D. orientis culture flocculated, with the minerals occurring as subhedral plate-like precipitates, which nucleated on the cell wall during exponential growth producing extensive mineral aggregates following cell autolysis and endospore release. In contrast, the D. desulfuricans culture produced fine-grained colloidal or platy iron sulfide precipitates primarily within the bulk solution. Mineral analysis by scanning electron microscopy – energy dispersive spectroscopy indicated that neither culture promoted advanced mineral development beyond a 1:1 Fe:S stoichiometry. This analysis did not detect pyrite (FeS2). The average Fe:S ratios were 1 : 1.09 ± 0.03 at 24 h and 1 : 1.08 ± 0.03 at 72 h for D. orientis and 1 : 1.05 ± 0.02 at 24 h and 1 : 1.09 ± 0.07 at 72 h for D. desulfuricans. The formation of “biogenic” iron sulfides by dissimilatory sulfate-reducing bacteria is influenced by bacterial cell surface structure, chemistry, and growth strategy, i.e., mineral aggregation occurred with cell autolysis of the gram-positive bacterium.
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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) | 0.000 | 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".