Upflow Reactors for Riparian Zone Denitrification
Bibliographic record
Abstract
We used permeable reactive subsurface barriers consisting of a C source (wood particles), with very high hydraulic conductivities ( approximately 0.1-1 cm s(-1)), to provide high rates of riparian zone NO3-N removal at two field sites in an agricultural area of southwestern Ontario. At one site, a 0.73-m3 reactor containing fine wood particles was monitored for a 20-mo period and achieved a 33% reduction in mean influent NO3-N concentration of 11.5 mg L(-1) and a mean removal rate of 4.5 mg L(-1) d(-1) (0.7 g m(-2) d(-1)). At the second site, four smaller reactors (0.21 m3 each), two containing fine wood particles and two containing coarse wood particles, were monitored for a 4-mo period and were successful in attenuating mean influent NO3-N concentrations of 23.7 to 35.1 mg L(-1) by 41 to 63%. Mean reaction rates for the two coarse-particle reactors (3.2 and 7.8 mg L(-1) d(-1), or 1.5 and 3.4 g m(-2) d(-1)) were not significantly different (p > 0.2) than the rates observed in the two fine-particle reactors (5.0 and 9.9 mg L(-1) d(-1), or 1.8-3.5 g m(-2) d(-1)). A two-dimensional ground water flow model is used to illustrate how permeable reactive barriers such as these can be used to redirect ground water flow within riparian zones, potentially augmenting NO3- removal in this environment.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".