Modeling Sedimentation in Underground Stormwater Detention Chamber Systems
Bibliographic record
Abstract
In Ontario, underground stormwater detention chamber systems (USDC) are a somewhat new alternative to wet detention ponds for the detention and treatment of urban stormwater runoff. Few tools are available for designers to predict the removal of contaminants from USDC. A conceptual model was developed to fill this void and make USDC a more approachable solution to stormwater management. The model for underground detention sedimentation (MUDS) predicts the removal efficiency of total suspended solids through sedimentation based on the hydraulic properties of USDC. As runoff enters the USDC, waves of particles are released, the pathline of these particles are tracked along vertical and longitudinal axes as they move through the USDC to determine whether a given diameter of particle is removed by sedimentation. Based on the particle size distribution and the density of the particles, the mass removal efficiency is determined. The model was found to adequately predict the hydraulics and treatment efficiency of USDC.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".