Distinct oxygen isotope fractionations driven by different electron donors during microbial nitrate reduction in lake sediments
Bibliographic record
Abstract
Abstract Microbial nitrate reduction can be driven by organic carbon oxidation, as well as by inorganic electron donors, such as reduced forms of sulfur and iron. An apparent inverse oxygen isotope fractionation effect was observed during nitrate reduction in sediment incubations from five sampling sites of a freshwater lake, Hongze Lake, China. Incubations with organic and inorganic electron donor additions were performed. Especially, the inverse oxygen isotope effect was intensified after glucose addition, whereas the incubations with sulfide and Fe 2+ showed normal fractionation factors. Nitrate reductase encoding genes, napA and narG , were analysed with metagenomics. Higher napA / narG ratios were associated with higher oxygen fractionation factors. The most abundant clade (59%) of NapA in the incubation with glucose was affiliated with Rhodocyclales . In contrast, it only accounted for 8%–9% of NapA in the incubations with sulfide and Fe 2+ . Differences in nitrate reductases might explain different oxygen isotope effects. Our findings also suggested that large variance of O‐nitrate isotope fractionations might have to be considered in the interpretation of natural isotope records.
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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 | 0.000 | 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) | 0.000 | 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".