Evaluation for the Leaching of Cr from Coal Gangue Using Expansive Soils
Bibliographic record
Abstract
Coal gangue can cause significant heavy metal pollution in mining areas, which would have a negative impact on the environment and human health. The objective of this research is to investigate the relationship between expansive soil amount and the leaching behavior of Chromium from coal gangue and the engineering properties of coal gangue used as building materials. The leaching behavior of Chromium from coal gangue was observed using atomic absorption spectrometry. A column leaching experiment was conducted to examine the impact of leaching time and heavy metal concentration. Furthermore, the unconfined compressive strength test was employed to evaluate the engineering properties of coal gangue with expansive soil. The results of the study demonstrate that pH of leachate solutions, leaching time, and expansive soil amounts in mixtures have important influence on Chromium concentration. The leachate solutions, which behave like alkaline, provide a positive environment for adsorbing Cr. Adding expansive soil can reduce leached concentrations of Chromium from coal gangue when compared to leachate of original coal gangue. It was found that 30% expansive soil was an improved solution because it delayed the cumulative concentration to reach the limitation line. Moreover, the unconfined compressive strength of coal gangue was boosted through adding expansive soil.
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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) | 0.001 | 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".