<i>p</i>CO<sub>2</sub> and CO<sub>2</sub> fluxes of the metropolitan river network in relation to the urbanization of Chongqing, China
Bibliographic record
Abstract
Abstract Most rivers of the world are supersaturated with carbon dioxide (CO2), resulting in a large gas flux that has only recently been included in global carbon budgeting. However, little is known about CO2 emissions from urban river networks suffering anthropogenic disturbance in the context of global urbanization. We surveyed the partial pressure of carbon dioxide (pCO2) and CO2 flux from 84 locations in the Chongqing metropolitan river network, with an area of 5494 km2. The overall mean pCO2 and CO2 fluxes were 2152 µatm and 163.0 mol C m−2 yr−1, respectively, with 318.9 mol C m−2 yr−1 from rivers in the completely urban area and 49.6 mol C m−2 yr−1 from the least urbanized area. The riverine pCO2 level increased with the proportion of urban land, with 2–4 times higher CO2 flux in the urban areas than the remote rural ones. Sites with low flow velocity, narrow channel, and factitious bottom sediment appeared to be local hot spots of CO2 emission. The pCO2 and CO2 degassing was positively correlated with the nutrients content of surface water (i.e., nitrogen and phosphorus). Carbon was released to the atmosphere as CO2 from Chongqing metropolitan river network at a rate of 12.3 × 109 mol C yr−1. pCO2 exhibited a clear seasonality with lower values in March 2015 and June 2015 but a higher value in September 2014, which was coregulated by temperature, flood dilution, and in situ photosynthesis. The results highlight that rapid urbanization, with increasing nutrients loading and anthropogenic activities, will alter the carbon biogeochemical cycle in the terrestrial‐aqueous‐atmospheric ecosystem and then impact global CO2 budgets.
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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 | 0.002 |
| 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".