Denitrification and Organic Carbon Availability in Riparian Wetland Soils and Subsurface Sediments
Bibliographic record
Abstract
The influence of organic C quantity and quality on denitrification in riparian environments is poorly understood. We measured denitrification potential (DNP), organic matter, and several fractions of organic C in surface soils and subsurface sediments in a river riparian zone. Surface soils in conifer forest peat, mixed forest, and marsh sites had similar DNP, although mean organic matter ranged from 9.4% (marsh) to 19.6% (mixed forest) and 36.6% (peat). These soils also differed widely in organic C, water‐extractable C, and anaerobic mineralizable C. Mean DNP in peat at depths of 0.8 to 1.4 m was four times lower than in the surface peat. Mean organic matter and organic C were significantly greater in the deep peat than at the surface, whereas the other C fractions were similar. Mean organic matter content of buried channel sediments at depths of 2 to 3 m was 3.6%; however, mean DNP was 75 to 80 times lower than in the surface mixed forest and marsh soils. When the three surface soil sites were considered separately, anaerobic mineralizable C showed the highest correlation with DNP in the marsh soils ( r = 0.87) and the conifer peat soil ( r = 0.82). Water‐extractable C was also highly correlated with DNP in the marsh soils ( r = 0.81). Correlations between DNP and either organic matter or the three C fractions were not significant in the deep peat, whereas the former channel sediments showed a significant relationship between DNP and both organic matter ( r = 0.81) and water‐extractable C ( r = 0.81). These results show that C quantity and quality influence DNP, but no single index was a good predictor for all soil types.
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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".