Modeling Sedimentation in Underground Stormwater Detention Chamber Systems
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
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.
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 | 0.001 |
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