Assessment of Wood Chips and Agricultural Residues as Denitrifying Bioreactor Feedstocks for Use in the Canadian Prairies
Bibliographic record
Abstract
Highlights Performance of denitrifying bioreactors in Alberta was evaluated. Barley straw was more effective in reducing nitrate compared to wood chips. Hydraulic retention time, feedstock, and season are the primary factors affecting nitrate removal. Abstract. This study evaluated the performance of pilot-scale denitrifying bioreactors (LWD: 6 × 0.6 × 1m) filled with different carbon substrates, including barley straw, hemp straw, and woodchips, for removing dissolved nitrogen from simulated subsurface drainage at two representative geographic locations in Alberta. In this study, the bioreactors were tested under varying hydraulic retention times (4, 8, and 12 h) in the spring, summer, and fall of one year. Tracer studies were conducted to evaluate flow and dispersion characteristics. The mean of nitrate removal efficiency ranged from 19% to 87% during the spring, 44% to 95% during the summer, and 21% to 68% during the fall. We found that barley straw was more effective in reducing nitrate (45% to 95%) compared to wood chips (19% to 54%). This study is the first testing of the effect of different biomass types and hydraulic residence times on bioreactor performance in the Canadian prairies (Alberta) and will allow agricultural producers and regulators to assess the suitability of these systems within the region. Keywords: Bioreactor, Denitrification, Water quality, Wood chips, Agricultural residues, Subsurface Drainage.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".