Denitrification and Organic Carbon Availability in Riparian Wetland Soils and Subsurface Sediments
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| 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 it