Changes in soil organic carbon stocks under 10-year conservation tillage on a Black soil in Northeast China
Bibliographic record
Abstract
SUMMARY Biased assessment of tillage impacts on soil organic carbon (SOC) sequestration are often associated with a lack of information on the initial level of SOC stocks. The present study reported the changes in SOC concentrations and stocks following 10-year different tillage practices relative to the initial SOC levels. The tillage trial included no tillage (NT), ridge tillage (RT) and mouldboard plough (MP) on a Black soil (Hapludolls) in Northeast China. Results showed that tillage, soil depth and time significantly affected SOC concentration and SOC stock. Tillage and crop residue retention had great impacts on the SOC concentrations in the top 0·1 m layer. Compared with MP and NT, RT resulted in higher SOC concentration and SOC stock in the plough layer (0–0·2 m), which became more obvious with time. The soil under NT and RT had higher stratification ratios (SR) of SOC (SR, the ratio of SOC concentration in 0–0·05 m to that in 0·1–0·2 m) than under MP. Significant positive and nearly identical linear relationships between the SR of SOC and the duration of tillage practices occurred for both NT and RT soils; the increased SR in NT resulted from both SOC increase in surface and SOC decrease in subsurface soils, but in RT, the increased SR was only from a substantial SOC increase in surface soil. Accordingly, the present study highlights that RT was more helpful than NT in carbon sequestration for the studied Black soil in Northeast China.
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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 | 0.001 |
| 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".