Contrasting effects of corn straw biochar on soil water infiltration and retention at tilled and compacted bulk densities in the Yellow River Delta
Bibliographic record
Abstract
During field application of biochar, the bulk density of tilled soil initially decreases and then increases over time, until reaching the initial level of compacted soil. This study evaluated the optimal biochar particle size for promotion of water infiltration and retention in a saline soil with various bulk densities after application. Corn straw biochar, pyrolyzed at 450 °C for 0.5 h, was prepared in different particle sizes (S1 ≤ 0.25 mm, S2 = 0.25–1 mm, and S3 = 1–2 mm) and separately mixed into the 0–30 cm soil layer at two rates (R1 = 10 g kg−1 and R2 = 100 g kg−1), with tilled (D1 = 1.1–1.43 g cm−3) and compacted (D2 = 1.45 g cm−3) bulk densities. Five models were applied to simulate water infiltration into biochar-amended soils. Compared with the non-biochar control, the S1 treatment increased cumulative water infiltration by 41% (bulk density = 1.26 g cm−3) to 11% (bulk density = 1.45 g cm−3). However, the effect of the S3 treatment on cumulative water infiltration shifted from positive (+19.3%) to negative (–22.4%) with increasing bulk density. The S2 treatment resulted in the highest water retention at the tilled bulk density, whereas a significant increase (12.7%) in water retention was observed in the S1 treatment at the compacted bulk density. The Kostiakov–Lewis, Kostiakov, and United States Department of Agriculture – Natural Resources Conservation Service models performed better than the Philip and Horton models to describe the relationship between cumulative water infiltration and infiltration time, except for the D2R2S1 treatment. This study provides evidence for amelioration of saline soil by straw biochar in the Yellow River Delta.
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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".