Denitrification Potential in Relation to Lithology in Five Headwater Riparian Zones
Bibliographic record
Abstract
The influence of riparian zone lithology on nitrate dynamics is poorly understood. We investigated vertical variations in potential denitrification activity in relation to the lithology and stratigraphy of five headwater riparian zones on glacial till and outwash landscapes in southern Ontario, Canada. Conductive coarse sand and gravel layers occurred in four of the five riparian areas. These layers were thin and did not extend to the field-riparian perimeter in some riparian zones, which limited their role as conduits for ground water flow. We found widespread organic-rich layers at depths ranging from 40 to 300 cm that resulted from natural floodplain processes and the burial of surface soils by rapid valley-bottom sedimentation after European settlement. The organic matter content of these layers varied considerably from 2 to 5% (relic channel deposit) to 5 to 21% (buried soils) and 30 to 62% (buried peat). Denitrification potential (DNP) was measured by the acetylene block method in sediment slurries amended with nitrate. The highest DNP rates were usually found in the top 0- to 15-cm surface soil layer in all riparian zones. However, a steep decline in DNP with depth was often absent and high DNP activity occurred in the deep organic-rich layers. Water table variations in 2000-2002 indicated that ground water only interacted frequently with riparian surface soils between late March and May, whereas subsurface organic layers that sustain considerable DNP were below the water table for most of the year. These results suggest that riparian zones with organic deposits at depth may effectively remove nitrate from ground water even when the water table does not interact with organic-rich surface soil horizons.
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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 | 0.000 |
| 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".