TBA Physicochemical Distribution of Metals in the Water Phase of Catch Basin Mixtures
Bibliographic record
Abstract
A mixture of sediment and water is formed during the cleansing of catch basins. This paper discusses the concentration levels and distribution of numerous metals and organic carbon (OC) in the water phase of this mixture. The results show that due to the high concentrations of metals in the water phase, the catch basin mixture should be treated before it reaches a recipient. Three sites with different types of area and traffi c intensity were sampled. Four fractions were analyzed: unfi ltered, dissolved (<0.2 μm), colloidal (0.22 μm to 1 kD [kilodalton]), and truly dissolved (<1 kD). The results of the unfi ltered fraction show high concentrations of metals and OC in the catch basin mixture. A comparison of Canadian and Swedish Environmental Protection Agency guidelines and the catch basin mixtures shows that concentrations exceeded the threshold values for As, Cd, Cr, Cu, Ni, Pb, and Zn. Compared with samples from a reference lake in the area, the unfi ltered fraction showed high concentrations of all elements. OC seems to have a large impact on the overall speciation of trace metals in the catch basin mixture. To trace the sources of the particulate fraction in the unfi ltered samples, Al-normalization was used. Al-normalization indicated that Ca, K, Mg, Na, Mn, Ba, Co, and Cr concentrations could be explained by mineral particles used as traction control. Furthermore, the trace elements As, Cu, Pb, Zn, and Ni were all enriched in the catch basin mixture. Key words: stormwater, speciation, sources, ultrafi ltration
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".