Performance of denitrifying bioreactors in southern Alberta
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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