Utilization of Phototrophic Bacteria to Enhance Carbon Sequestration in Rice Paddy
Bibliographic record
Abstract
Rice paddies are a major source of agricultural greenhouse gas emissions, primarily caused by the proliferation of anaerobic, methanogenic bacteria during prolonged inundation. Phototrophic bacteria utilize light energy for metabolism and are potential candidates for carbon and nitrogen fixation, and reduction of methane gas emissions. We investigated the effect of applying the phototrophic bacterium Rhodopseudomonas palustris (PNSB) during the cropping period on soil organic carbon (SOC) and methane emissions for second-crop rice in the Tainan Guantian region. In the experimental group, PNSB was applied five times during the rice cultivation period. Compared to the control group, the experimental group demonstrated a significant reduction in methane emissions, especially in the tillering stage, where emissions averaged 37.26 ± 12.97 g-CH4/m2/season compared to 49.48 ± 25.06 g-CH4/m2/season of the control group. Over the entire growing season, the experimental group reduced the emission of 3.05 Mg·CO2e/ha. Additionally, administering PNSB improved soil carbon sequestration, from 4.89 tons-C/ha in the control group to 17.45 tons-C/ha. The phototrophic bacterium PNSB was beneficial for soil carbon sequestration and reducing greenhouse gas emissions. However, further research is required to optimize the methodology of applying phototrophic bacteria for agricultural purposes.
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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) | 0.001 | 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".