A storm water basin model using settling velocity distribution
Bibliographic record
Abstract
Quantifying processes that affect the fate of particles in storm water basins is a complex but necessary step to predict the effect of various pollutants on receiving waters. A dynamic model for storm water basins taking advantage of the experimental fractionation of particles in different settling velocity classes has been developed to describe the water quality dynamics in the basin. This paper is focused on the calibration of the model using total suspended solids (TSS) time series data and settling velocity distribution data obtained from ViCAs (vitesse de chute en assainissement) tests. Experimental sampling campaigns have been conducted at an actual storm water basin to identify the TSS behaviour under various operational conditions. For one set of experiments, the outlet was always open, and for another, the outlet was kept closed to allow settling before release to the receiving water. The experimental results reveal spatial heterogeneity of the particle concentrations in the basin during the initial phases of water retention for the closed outlet sampling campaign. A calibration procedure is proposed to fit the model to the experimental data. This model was found able to reproduce both open and closed outlet TSS concentration time series with only three particle classes.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".