Biochar application stabilized the heavy metals in coal mined soil
Bibliographic record
Abstract
The waste of coal mining activities causes accumulation of hazardous elements in soil for plants. Biochar is considered an important soil remediation strategy to stabilize the heavy metals. The aim of this study was to quantify the effect of biochar sources and rate on the heavy metal stabilization in coal-contaminated soil. Biochars of three feedstocks (maize straw (MBC), rice straw (RBC), and sugarcane bagasse (SBC)) with four levels (0%, 0.5%, 1%, and 2%, i.e., 0, 10, 20, and 40 ton ha−1) were applied to two types of soils (naturally contaminated soil (NCS) versus artificially contaminated soil (ACS) spiked with Cd, Cu, Cr, and Pb). Plastic pots were incubated at 30% field capacity for 90 days at 25 °C, and soil pH, electrical conductivity (EC), and heavy metals concentration were measured after 1, 4, 8, and 12 weeks. Among the biochars, RBC showed maximum immobilization of Cd, Pb, Cu, and Cr as compared with MBC and SBC. Similarly, biochar application increased heavy immobilization, being maximum at 2% (40 ton ha−1) rate compared with control. The pH of both soils with biochar addition increased as compared with control. The remediation effect of biochar on heavy metal stabilization was positive over time. The higher rate (40 ton ha−1) of RBC for ACS and MBC for NCS could be used effectively for heavy metal stabilization.
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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".