Full Scale Up-Flo Filter Field Verification Tests
Bibliographic record
Abstract
Full scale field tests of the Up-Flo filter have been conducted at the Bama Belle Riverwalk parking lot test site in Tuscaloosa, Alabama for the past several years.Forty storm events have been monitored and sampled, and these field performance results indicate that the Up-Flo filter has excellent removals for particulates during a wide-range of hydraulic-rainfall conditions.Total suspended solids removal was about 82% for influent concentrations ranging from 11 mg/L to 571 mg/L; suspended solids concentration flow-weighted removal was about 90%; flow-weighted turbidity removal about 61%.Particle size distribution analysis determined that the influent median particle size of the 40 sampled storms was about 460µm and about 45 µm for the effluent.Nutrient reductions were about 37% and 17% for total nitrogen and total phosphorus, respectively.Metal reductions ranging from about 54% to 76% for total copper, 67% to 98% for total lead, and 79% to 83% for total zinc.Bacteria reductions were 53% for E. coli.and 57% for Enterococci.Additional event data are being collected and will be further analyzed to examine performance behavior as a function of a wide range of rainfall and runoff conditions.
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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 | 0.000 | 0.000 |
| 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 | 0.001 |
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".