Relationships between methane emissions and soil microorganisms in a double-rice field in southern subtropical China
Bibliographic record
Abstract
Methane is produced in anaerobic environments by obligate anaerobic microorganisms through the decomposition of organic matters. To investigate the regression relationships between methane emissions and soil microbes in a double-rice paddy under various field practices, a two-year study was conducted to investigate the seasonal variation of methane emissions and the total activities of soil microbes (TASM) as well as the populations of methanogens (PMET) using the static closed-chamber-GC (gas chromatography) and the most probable number methods. Seven management practices were included in this study to look at the average effect of field treatments on methane emissions and TASM as well as PMET, viz. CWS (conventional tillage + without straw residues + urea), NWS (no tillage + without straw residues + urea), SCU (conventional tillage + without straw residues + controlled-release urea), HN (high stubbles + no tillage + urea), HC (high stubbles + conventional tillage + urea), SN (straw cover + no tillage + urea) and SNF (straw cover + no tillage + urea + continuous flooding). The daily average values of methane emissions and TASM as well as PMET from seven treatments were used for the analysis. Regression analysis was conducted using the R statistical software. Different field practices have significant effect on methane flux and TASM and PMET and similar seasonal variations of methane flux and TASM as well as PMET were found during the rice-growing seasons. Pronounced positive correlations between methane flux and TASM, and PMET were observed. Such relationships can be well described by the exponential or quadratic polynomial models, respectively. Regression analysis indicated that PMET could explain individually at least 97% of variance of methane flux (R2 = 0.97, P < 0.001), while the fitting precision of multiple nonlinear regression model for methane flux with two predictors of TASM and PMET was slightly higher than the univariate regression analysis (R2 = 0.98, P < 0.001). However, as we know, methane emissions from paddy soils are affected by many factors, of which TASM and PMET are the most direct influential variants. In order to reasonably reveal the interactions between methane emissions and environmental factors, the multivariate nonlinear regression analysis should be carried out based on data derived from the extensive field experiments rather than few laboratory trials.
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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".