Vertical distribution of Fe and Fe(III)-reducing bacteria in the sediments of Lake Donghu, China
Bibliographic record
Abstract
In lake sediments, iron (Fe) is the most versatile element, and the redox cycling of Fe has a wide influence on the biogeochemical cycling of organic and inorganic substances. The aim of the present study was to analyze the vertical distribution of Fe and Fe(III)-reducing bacteria (FeRB) in the surface sediment (30 cm) of Lake Donghu, China. At the 3 sites we surveyed, FeRB and Fe(II)-oxidizing bacteria (FeOB) coexisted in anoxic sediments. Geobacter-related FeRB accounted for 5%-31% of the total Bacteria, while Gallionella-related FeOB accounted for only 0.1%-1.3%. A significant correlation between the relative abundance of poorly crystalline Fe and Geobacter spp. suggested that poorly crystalline Fe favored microbial Fe(III) reduction. Poorly crystalline Fe and Geobacter spp. were significantly associated with solid-phase Fe(II) and total inorganic phosphorus levels. Pore water Fe(II) concentrations negatively correlated with NO3(-) at all sites. We concluded that Geobacter spp. were abundant in the sediments of Lake Donghu, and the redox of Fe might participate in the cycling of nitrogen and phosphorus in sediments. These observations provided insight into the roles of microbial Fe cycling in lake sediments.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".