Low contribution of internal metabolism to carbon dioxide emissions along lotic and lentic environments of a Mediterranean fluvial network
Bibliographic record
Abstract
Abstract Inland waters are significant sources of carbon dioxide (CO2) to the atmosphere. CO2 supersaturation and subsequent CO2 emissions from inland waters can be driven by internal metabolism, external inputs of dissolved inorganic carbon (DIC) derived from the catchment, and other processes (e.g., internal geochemical reactions of calcite precipitation or photochemical mineralization of organic solutes). However, the sensitivity of the magnitude and sources of CO2 emissions to fluvial network hydromorphological alterations is still poorly understood. Here we investigated both the magnitude and sources of CO2 emissions from lotic (i.e., running waters) and lentic (i.e., stagnant waters associated to small dams) waterbodies of a Mediterranean fluvial network by computing segment‐scale mass balances of CO2. Our results showed that sources other than internal metabolism sustained most (82%) of the CO2 emissions from the studied fluvial network. The magnitude and sources of CO2 emissions in lotic waterbodies were highly dependent on hydrology, with higher emissions dominated by DIC inputs derived from the catchment during high flows and lower emissions partially fueled by CO2 produced biologically within the river during low flows. In contrast, CO2 emissions in lentic waterbodies were low, relatively stable over the time and the space, and dominated by DIC inputs from the catchment regardless of the different hydrological situations. Overall, our results stress the sensitivity of fluvial networks to human activities and climate change and particularly highlight the role of hydromorphological conditions on modulating the magnitude and sources of CO2 emissions from fluvial networks.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".