Utilizing settling tests to design a conventional upflow settling tank modified with inclined plates
Bibliographic record
Abstract
This paper examines the relationships between the turbidity removal efficiency (TRE), the surface overflow rate (SOR), and the detention time (D(t)) in settling column and jar tests, as well as the performance of a conventional upflow settling tank modified with inclined plates in the upper zone. The experimental results showed that the SOR obtained from the flocculent settling column test can be increased by 30% and the corresponding D(t) can be decreased by 75% with a variation in TRE of less than 7%. The TRE of flocculent settling in the jar test coincided with the performance of the modified upflow settling tank, while the results of the settling column test were slightly different. For plain settling, the SOR obtained from jar and settling column tests should be divided by 3 and 2, respectively, before possible use in the design of the modified upflow settling tank. Two empirical models with 1.0% error in the TRE predictions were developed to facilitate the design of the modified upflow settling tank.
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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 | 0.001 | 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".