Effects of Aged Biochar on Remediation of Cd-Contaminated Soil and Greenhouse Gas Emission in Chinese Cabbage (Brassica chinensis L.) Growth
Bibliographic record
Abstract
Biochar has demonstrated effectiveness in environmental remediation. However, the physicochemical properties of biochar change with natural aging, which potentially impacts its efficacy. This study was designed to evaluate the effects of aged biochar (at 1% and 5% rates) on the growth of Chinese cabbage, greenhouse gas emission, and Cd remediation in soils. Canada goldenrod (Solidago canadensis L.) feedstock biochar was subjected to three artificial aging processes (freeze–thaw cycle, dry–wet cycle, and hydrogen peroxide oxidation) to prepare aged biochar. Results showed that aging significantly altered properties and structure of biochar. Biochar addition had no effect on CH4 emissions, but it decreased cumulative N2O emission (all treatments) and increased cumulative CO2 emission (only the pristine biochar at 5% application rate). Aged biochar showed no effect on microbial life strategy and Shannon index. However, PB-5% application shifted the life history strategies of A-strategists (resource acquisition microbe) towards Y-strategists (high-yield microbe) such as Proteobacteria, Gemmatimonadota, Bacteroidota, Firmicutes and Actinobacteriota, which partially attributed to the enhanced soil CO2 emission. Aged biochar reduced plant uptake Cd and soil available Cd concentrations by up to 36.6% and 34.0%, respectively, ascribing to improved soil physicochemical properties and functional bacterial abundance.
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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".