Bioretention cell efficacy in cold climates: Part 2 — water quality performance
Bibliographic record
Abstract
Field and column experiments were conducted to test the ability of bioretention cells to improve urban runoff quality. The effects of cold climate conditions, media depth, long-term operation, and extreme loading were analyzed. Field experiments demonstrated significant decrease in contaminant mass, which was a function of the large runoff volume capture. Significant decreases in concentration for sediment (96%), biochemical oxygen demand (BOD, 8%), and total phosphorus (0.6%) were noted. Long-term simulation experiments demonstrated a decrease in effluent concentration over time, suggesting a dependence on media chemistry. Sediment and BOD capture remained high throughout the testing period. Media depth did not impact performance in laboratory experiments. Extreme loading experiments proved that the effluent concentration of contaminants was independent of the influent concentration. Cold climate conditions did not have a significant impact on performance in both field and column experiments.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 |
| 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 teacher head, 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".