Effect of Contaminated Suspended Solids on Water and Sediment Qualities and Their Treatment
Bibliographic record
Abstract
Abstract Suspended solids (SS) have been discharged into water areas such as rivers, lakes, and ponds. The SS adsorb various contaminants such as heavy metals and nutrients and also form sediments by sedimentation. Therefore, contaminated SS will be able to influence not only the water quality, but also the sediment quality. In order to understand the effect of contaminated SS on the water and the sediment, SS and sediment samples were obtained from the des Hurons River in Canada. In addition, laboratory filtration tests were performed to develop a technique for removal of the SS. A downward filtration system was used with a nonwoven geotextile as a filter medium. The apparent opening size (AOS) and the thickness of the filter were 150 μm and 0.2 cm, respectively. For the investigation, the results showed that both SS and the sediments contained heavy metals with concentrations in the SS higher. In particular, zinc concentrations of the SS were approximately from two to five times higher than the Canadian guideline for sediments. In addition, it was found that SS concentrations were associated with chemical oxygen demand (COD) and total phosphorus (T-P) concentrations. Therefore, it was found that SS can play an important role in the water and the sediment qualities. The laboratory filtration tests showed the SS were reduced from 32 mg/L to 2 mg/L or less by the nonwoven filter. Thus, SS removal will improve not only the water quality, but also the quality of the bottom sediments.
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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 | 0.000 | 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) | 0.001 | 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".