Basin‐Scale CO<sub>2</sub> Emissions From the East River in South China: Importance of Small Rivers, Human Impacts and Monsoons
Bibliographic record
Abstract
Abstract Riverine carbon dioxide (CO2) emissions are an essential component of the riverine carbon cycle, but an accurate assessment of riverine CO2 emission fluxes is still hindered by the spatial and temporal variations among river basins caused by differences in climate, watershed characteristics, and human activity. Here, we evaluate the riverine CO2 flux from the subtropical East River Basin (ERB) in south China, a region strongly affected by monsoon climate and anthropogenic land use changes. Our results suggest small rivers are major contributors to riverine CO2 emissions, even with relatively low CO2 concentrations and small water surface areas (SAs). They contribute disproportionately to 74.4% of the total fluxes due to high gas transfer velocity (k) across the water‐air interface. Land use changes have substantially enhanced CO2 emissions from river networks. Normalized areal riverine CO2 fluxes in the urban‐ and cropland‐dominated Middle and Lower ERB (27.6 and 39.4 g C m−2 yr−1) were two and three times higher than the 9.1 g C m−2 yr−1 in the forest‐dominated Upper ERB. Due to the larger water SA and higher k caused by monsoon‐induced precipitation, the East River acts as a stronger carbon source during the wet season, emitting 0.67 Tg C yr−1 to the atmosphere, which is about twice that during the dry season (0.33 Tg C yr−1). Our study illustrated how monsoon climate and land use in the ERB have regulated its riverine CO2 emissions. Our findings also provided valuable insights into the role of small rivers in the basin‐wide carbon cycle.
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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 | 0.001 |
| 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) | 0.001 | 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".