Diversity decoupled from sulfur isotope fractionation in a sulfate‐reducing microbial community
Bibliographic record
Abstract
Abstract The extent of fractionation of sulfur isotopes by sulfate‐reducing microbes is dictated by genomic and environmental factors. A greater understanding of species‐specific fractionations may better inform interpretation of sulfur isotopes preserved in the rock record. To examine whether gene diversity influences net isotopic fractionation in situ, we assessed environmental chemistry, sulfate reduction rates, diversity of putative sulfur‐metabolizing organisms by 16S rRNA and dissimilatory sulfite reductase ( dsrB ) gene amplicon sequencing, and net fractionation of sulfur isotopes along a sediment transect of a hypersaline Arctic spring. In situ sulfate reduction rates yielded minimum cell‐specific sulfate reduction rates < 0.3 × 10 −15 moles cell −1 day −1 . Neither 16S rRNA nor dsrB diversity indices correlated with relatively constant (38‰–45‰) net isotope fractionation (ε 34 S sulfide‐sulfate ). Measured ε 34 S values could be reproduced in a mechanistic fractionation model if 1%–2% of the microbial community (10%–60% of Deltaproteobacteria) were engaged in sulfate respiration, indicating heterogeneous respiratory activity within sulfate‐reducing populations. This model indicated enzymatic kinetic diversity of Apr was more likely to correlate with sulfur fractionation than DsrB. We propose that, above a threshold Shannon diversity value of 0.8 for dsrB , the influence of the specific composition of the microbial community responsible for generating an isotope signal is overprinted by the control exerted by environmental variables on microbial physiology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.000 | 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".