Evaluation of Heavy Metals Leakage from Concretes Containing Municipal Wastewater Sludge
Bibliographic record
Abstract
Nowadays an important environmental concern is to dispose of municipal wastewater sludge containing toxic heavy metals. These trace elements could be highly hazardous due to their insolubility, high toxicity, bioaccumulation and carcinogenic characterization. One of the latest common ways of sludge disposal is to use in construction materials such as concrete. The aim of this study is to examine leaching of heavy metals from concretes containing sewage sludge. For this purpose, concrete cubes were constructed with different percentages of wastewater sludge (0, 25, 50, 75, 100) replaced with water. Slump and compressive strength of the samples were measured after curing times of 7, 28 and 90 days. Standard test method of NEN 7345 was used to evaluate the possibility of heavy metals leakage out of concrete including Cr+6, Cu+2, Zn+2, Fe+2, Se+2 and Ba+2. Results presented insignificant amount of heavy metals leaking out of concretes according to EPA standards.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".