Soil CO <sub>2</sub> emission and carbon budget of a wheat/maize annual double-cropped system in response to tillage and residue management in the North China Plain
Bibliographic record
Abstract
To investigate the impacts of tillage and crop residue managements on soil CO2 emission and C budget in a wheat (Triticum aestivum L.)/maize (Zea mays L.) double-cropped system in the North China Plain (NCP), a field experiment was conducted consisting of four treatments: tillage with crop residues retention (CT+), tillage with crop residues removal (CT−), no-till with crop residues retention (NT+), and no-till with crop residues removal (NT−). Daily soil CO2 fluxes changed with crop growing stage and peaked during the most vigorous growth of period, fluxes in maize season were higher than those in wheat season. Compared to the tilled soils, cumulative CO2 emissions were significantly lower under no-till treatments. The largest cumulative CO2 emission occurred under CT+ (65 g CO2-C m−2 y−1) and the smallest was under NT+ (39 g CO2-C m−2 y−1). After 5 years of the experiment, soil organic carbon (SOC) sequestration were greater with crop residues retention (CT+ and NT+) than with crop residues removal (CT− and NT−), the maximum SOC stock was in NT+ (5940 g C m−2) while the minimum was in CT− (3635 g C m−2). NT+ could help to mitigate CO2 emission in the annual wheat/maize double-cropping system of the area.
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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".