Performance of denitrifying bioreactors in southern Alberta
Bibliographic record
Abstract
Denitrifying bioreactors are an edge-of-field passive treatment technology that can reduce nutrient export from subsurface drainage waters to aquatic ecosystems. This technology is gaining popularity in many parts of the world including eastern Canada, but has not gained widespread acceptance in the Canadian prairies. This study evaluated the performance of pilot-scale denitrifying bioreactors for removing nitrate under agricultural field conditions in southern Alberta. Local agricultural residues– barley straw and hemp straw– were tested in comparison to wood chips for nutrient removal potential under varying retention times and temperatures during the growing season. Results from this study identified that the primary factors affecting nitrate-nitrogen removal in this region were temperature, flow rate, carbon source material and the age of the materials in the bioreactor. Both agricultural residues exceeded wood chip performance in the first year of operation, but all fill materials performed similarly in the second year of operation– the percent reduction of nitrate-nitrogen dropped from 72% to 34% and 55% to 32% for barley straw and hemp straw, respectively, while increasing from 27% to 29% for wood chips. These results indicate that more research is needed on the use of barley straw and hemp straw in bioreactors after an overwinter period.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.001 | 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".