A Water Elutriation Apparatus for Measuring Settling Velocity Distribution of Suspended Solids in Combined Sewer Overflows
Bibliographic record
Abstract
Abstract An elutriation apparatus has been proposed for determining the settling velocity distributions of suspended solids in combined sewer overflows (CSOs). The apparatus consisted of a series of interconnected cylindrical settling chambers, through which the sample was drawn using a peristaltic pump attached to the outlet of the last settling chamber. Since the diameters of the cylinders were progressively increasing, sediment with different settling velocities settled in different cylinders. By measuring the amount of sediment in each cylinder, the settling velocity distribution was deduced. The apparatus was tested using samples from a CSO outfall in Hamilton, Ontario, Canada. The test results showed that the apparatus performed well for determining settling characteristics of suspended solids and chemical distribution in different settling fractions. The effectiveness of polymer addition to enhance flocculation and settling of CSOs was also tested in this apparatus. The elutriation apparatus proposed here offers several advantages over the traditional methods (i.e., settling columns) of measurements of settling velocity distributions. The main advantage is its ability to measure the distributions under dynamic conditions, i.e., in the presence of flow. Consequently, the elutriation apparatus reproduces flow conditions in actual settling tanks better than conventional settling columns.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".