Note on sediment removal efficiency in oil–grit separators
Bibliographic record
Abstract
Oil-grit separators (OGSs) are one type of best management practice, designed to remove oil and grit from stormwater runoff (e.g., from parking lots and paved roads). This note examines scaling parameters for OGS removal efficiency. Three dimensionless parameters are chosen as scaling parameters: Hazen number (Ha), Reynolds number (Re) and Froude number (Fr). The Hazen number is a ratio of hydraulic residence time to particle settling time. The Reynolds number measures the surrounding turbulence effects on sediment removal efficiency. The Froude number represents the ratio of inertial and gravitational forces, which indicates the influence of gravity on fluid motion. The collected data from the literature on sediment removal in OGSs can be represented by a single curve when the Hazen, Reynolds, and Froude numbers are combined into a new scaling parameter (HRF = Ha(Re/Fr)). A general form is proposed to correlate the sediment removal efficiency with this new parameter. This generalized prediction method can be used as a preliminary performance indicator for OGS units. The obtained curve can also be used to adjust raw laboratory and field measurement data to improve the evaluation of the performance of various OGSs.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.001 |
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 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".