Mollisol Erosion-Driven Efflux of Energetic Organic Carbon and Microflora Increases Greenhouse Gas Emissions from Cold-Region Rivers
Bibliographic record
Abstract
Massive soil erosion occurs in the world’s Mollisol regions due to land use change and climate warming. The migration of Mollisol organic matter to river systems and subsequent changes in carbon biogeochemical flow and greenhouse gas fluxes are of global importance but little understood. By employing comparative mesocosm experiments simulating varying erosion intensity in Mollisol regions of northeastern China, this research highlights that erosion-driven export and biomineralization of terrestrial organic matter facilitates CO 2 and CH 4 emission from receiving rivers. Stronger Mollisol erosion, as represented by a higher soil-to-water ratio in suspensions, increased CO 2 efflux, particularly for the paddy Mollisols. This is mechanistically attributable to increased bioavailability of soluble organic carbon in river water that is sourced back to destabilized organic matter, especially from the cultivated Mollisols. Concurrent changes in microbial community structure have enhanced both aerobic and anaerobic processes as reflected by the coemission of CO 2 and CH 4 . Higher greenhouse gas effluxes from paddy Mollisol suspensions suggest that agricultural land use by supplying more nitrogen-containing, higher-free-energy organic components may have enhanced microbial respiration. These new findings highlight that Mollisol erosion is a hidden significant contributor to greenhouse gas emissions from river water, given that the world’s four major Mollisol belts are all experiencing intensive cultivation.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".