Contribution of winter soil respiration to annual soil CO<sub>2</sub> emission in a Mollisol under different tillage practices in northeast China
Bibliographic record
Abstract
Winter soil CO2 emission is a very important component of the annual carbon budgets, however, almost no information on winter CO2 emission is available from the cropland soil in northeast China. In this study, soil CO2flux was measured for a 2‐year period from an ongoing tillage trial on Black soil in northeast China to quantify seasonal patterns in soil CO2 flux rate and wintertime contribution to annual soil respiration. Average soil CO2 flux rates in the winter (November to March) were between 0.64 to 1.22 g CO2 m−2 d−1, in the non‐growing season (October and April) were 2.09–3.56 g CO2 m−2 d−1, whereas in the growing season (May to September) they were between 10.9 to 12.7 g CO2 m−2 d−1, with no significant differences among tillage treatments. Total winter, non‐growing and growing season soil CO2 emissions were 0.28–0.45 Mg C ha−1, 0.36–0.53 Mg C ha−1, and 4.52–5.55 Mg C ha−1, respectively, among tillage treatments. The contributions of winter soil respiration to annual soil CO2emission ranged from 5.1 to 7.1%, and the non‐growing season emission ranged from 11.4 to 15.2% among tillage treatments. Our results indicate that in northeast China, cropland Black soil continuously emits CO2throughout the non‐growing season, and the wintertime soil respiration plays a significant role in annual soil carbon budgets. Hence winter soil CO2 emission must be taken into consideration when the role of the soil ecosystem is assessed as either a sink or source of CO2 to the atmosphere.
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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 | 0.000 |
| 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".