Effectiveness of Different Sources of Biochar for Immobilizing Mercury in Soil from Artisanal and Small-Scale Gold Mining Areas in Taliwang Village of West Sumbawa Regency, Indonesia
Bibliographic record
Abstract
Mercury (Hg) contamination in soil can significantly harm the environment, food chain, and human health.Therefore, affordable, effective, long-lasting cleanup technologies are needed.Hg-contaminated soil taken from a former artisanal and small-scale gold mining (ASGM) in Taliwang Village, West Sumbawa District, West Nusa Tenggara Province, was used to compare the effectiveness of three types of biochar made from local agricultural wastes, namely corn cob (CC), rice husk (RH), and coconut shell (CS) as mercury immobilizer in a leaching experiment of the Hg-contaminated soil mixed with the biochar in three soil layers (0-10, 10-25, 25-50 cm).The results indicated that CC was more successful in immobilizing Hg in soil than RH and CS, revealed by the lowest Hg content in the leachate of CC-treated soil.SEM (scanning electron microscopy) and FTIR (Fourier Transform Infrared Spectroscopy) characterization of the biochar reveal that CC is more porous and has a higher content of hydroxyl groups than RH and CS, which support CC's highest capability in immobilizing Hg in soil.The study highlights the significance of biochar from agricultural wastes for mercury remediation in soil and suggests the possible use of CC biochar in maximizing the efficiency of mercury remediation in 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 | 0.001 | 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".