Effect of biochar on permeability of compacted soil and its microscopic mechanism
Bibliographic record
Abstract
Biochar has attracted much attention in the field of geo-environmental engineering as a carbon-neutral alternative for soil amendment. In this study, the effects of biochar dosage, type, and particle size on the permeability and physical properties of compacted silty clay were investigated. And the prediction model of permeability coefficient of biochar-amended soil (BAS) was established. Based on the microscopy test results, the influence mechanism of biochar on the permeability of compacted soil was analyzed. Results revealed that adding different biochar types affected the consistency limits, specific gravity, and compaction curves of soil obviously. As the biochar content increased, the permeability coefficient of compacted BAS initially decreased before increasing. The permeability coefficient of compacted BAS with peanut-shell biochar was much higher than with other biochar types. Additionally, reducing the particle size of biochar can effectively decrease the permeability coefficient of BAS due to the pore-filling effect. Microscopy test results show that the permeability coefficient of BAS is affected by both the most probable pore size and the main-peak area of the pore-size distribution curve. Results suggest that controlling the biochar content at 5% and reducing the particle size of biochar are more beneficial to improve the impermeability of compacted BAS.
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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 | 0.001 |
| 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".