Low temperature S0 biomineralization at a supraglacial spring system in the Canadian High Arctic
Bibliographic record
Abstract
Elemental sulfur (S(0) ) is deposited each summer onto surface ice at Borup Fiord pass on Ellesmere Island, Canada, when high concentrations of aqueous H(2) S are discharged from a supraglacial spring system. 16S rRNA gene clone libraries generated from sulfur deposits were dominated by β-Proteobacteria, particularly Ralstonia sp. Sulfur-cycling micro-organisms such as Thiomicrospira sp., and ε-Proteobacteria such as Sulfuricurvales and Sulfurovumales spp. were also abundant. Concurrent cultivation experiments isolated psychrophilic, sulfide-oxidizing consortia, which produce S(0) in opposing gradients of Na(2) S and oxygen. 16S rRNA gene analyses of sulfur precipitated in gradient tubes show stable sulfur-biomineralizing consortia dominated by Marinobacter sp. in association with Shewanella, Loktanella, Rubrobacter, Flavobacterium, and Sphingomonas spp. Organisms closely related to cultivars appear in environmental 16S rRNA clone libraries; none currently known to oxidize sulfide. Once consortia were simplified to Marinobacter and Flavobacteria spp. through dilution-to-extinction and agar removal, sulfur biomineralization continued. Shewanella, Loktanella, Sphingomonas, and Devosia spp. were also isolated on heterotrophic media, but none produced S(0) alone when reintroduced to Na(2) S gradient tubes. Tubes inoculated with a Marinobacter and Shewanella spp. co-culture did show sulfur biomineralization, suggesting that Marinobacter may be the key sulfide oxidizer in laboratory experiments. Light, florescence and scanning electron microscopy of mineral aggregates produced in Marinobacter experiments revealed abundant cells, with filaments and sheaths variably mineralized with extracellular submicron sulfur grains; similar biomineralization was not observed in abiotic controls. Detailed characterization of mineral products associated with low temperature microbial sulfur-cycling may provide biosignatures relevant to future exploration of Europa and Mars.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".