Measuring solids concentrations in urban stormwater and snowmelt: a new operational procedure
Bibliographic record
Abstract
A comparative study of five methods measuring suspended sediment or solid concentrations in water-sediment mixtures indicated that, depending on the method used, broadly varying results can be obtained. For water-sediment mixtures containing sand size particles, the standard TSS method produced negatively biased results, accounting for 0 to 90% of the present solids; the negative bias directly depended on the magnitude of the sand fraction in the water-sediment mixture. The main reason for the differences between the TSS and the rest of the methods laid in the handling of samples; in the former methods, whole samples were analysed, whereas the TSS analysis was performed on sub-samples withdrawn from the water sample, the withdrawal process tending to exclude large particles. The methods using whole water-solid samples, rather than aliquots withdrawn from such samples, produced accurate estimates of solid concentrations, with a fairly good precision. Two whole-sample methods were studied in detail, a slightly modified standard SSC-B method and the newly proposed operational procedure referred to as the Multiple Filter Procedure (MFP), using three filters arranged in a series with decreasing pore sizes (25, 1.6 and 0.45 µm). Both methods assessed accurately concentrations of solids in a broad range of concentrations (200-8000 mg L(-1)) and particle sizes (0.063-4.0 mm). The newly introduced MFP was in good agreement with the SSC procedure, the differences between the two procedures not exceeding the standard bias defined for the SSC-B method. The precision of both SSC and MFP was generally better than ±10%. Consequently, these methods should be used when the total mass of transported solids is of interest.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".