Sulfate and Dissolved Organic Carbon Concentrations Drive Distinct Microbial Community Patterns in Prairie Wetland Ponds
Bibliographic record
Abstract
ABSTRACT Prairie wetland ponds on the Great Plains of North America offer a diverse array of geochemical scenarios that can be informative about their impact on microbial communities. These ecosystems offer invaluable ecological services while experiencing significant stressors, primarily through drainage and climate change. In this first study systematically combining environmental conditions with microbial community composition to identify various niches in prairie wetland ponds, sediments had higher microbial abundance but lower phylogenetic diversity in ponds with lower concentrations of dissolved organic carbon ([DOC]; 10–18 mg/L) and sulfate ([SO 4 2− ]; 37–58 mg/L) in water. As [DOC] and [SO 4 2− ] increased, there was an initial decline in abundance but not phylogenetic diversity. Maximum values of both abundance and phylogenetic diversity occurred between 56 and 115 mg/L [DOC] and 5,000–6,000 mg/L [SO 4 2− ] and decreased thereafter in ponds with 150–180 mg/L and 8,000–14,000 mg/L [DOC] and [SO 4 2− ], respectively. These findings confirm that environmental variables shape the microbial communities and that key microbial taxa involved in sulfur and carbon cycling dominated these ponds potentially impacting vital biogeochemical processes such as bioavailability of heavy metals, carbon sequestration, and methane emissions.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 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".