Stormwater runoff treatment using compost biofilters
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
This research evaluates the effectiveness of compost biofilters for the removal of suspended sediments from stormwater runoff. Receiving water quality concerns associated with increased construction activities in recent years in the Greater Toronto Area (GTA) has prompted government agencies and academia to research new stormwater treatment technologies. A sustainable, green technology has been developed that uses large volumes of compost material as engineered compost biofilters for stormwater runoff treatment. Field experiments were conducted in the summer of 2006 at the Guelph Turf Grass Institute to verify the sediment removal efficiency of the compost biofilter. The average sediment removal efficiency of the 20 cm (8") socks for 5, 10 and 15 rolls were 34%, 48%, and 60%, respectively. The average sediment removal efficiency for 45 cm (18") socks for 5, 10 and 15 rolls were 69%, 84% and 95%, respectively. The decrease in sediment removal efficiency of the biofilter over time was significant. The average sediment removal efficiency of 5 rolls of the 45 cm (18") sock started to decrease gradually from 70% to 62%, 58%, 56% and then 54% after 1, 5, 10, 15 and 20 consecutive runs. Sediment removal efficiency of sediment particles in the size range of clay was found to be 30%, while coarser particles such as fine silt and coarse silt had 50% and 80% removal efficiency, respectively. The results from the Polyacrylamide polymer (PAM) tests show significantly higher sediment removal efficiencies (more than 90%) and the optimum application rate for liquid PAM was around 5 mg/L.
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) | 0.000 | 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 it