Groundwater inflows control patterns and sources of greenhouse gas emissions from streams
Bibliographic record
Abstract
Abstract Headwater streams can be important sources of carbon dioxide (CO2) and methane (CH4) to the atmosphere. However, the influence of groundwater–stream connectivity on the patterns and sources of carbon (C) gas evasion is still poorly understood. We explored these connections in the boreal landscape through a detailed study of a 1.4 km lake outlet stream that is hydrologically fed by multiple topographically driven groundwater input zones. We measured stream and groundwater dissolved organic C (DOC), CO2, and CH4 concentrations every 50 m biweekly during the ice‐free period and estimated in‐stream C gas production through a mass balance model and independent estimates of aquatic metabolism. The spatial pattern of C gas concentrations was consistent over time, with peaks of both CH4 and CO2 concentrations occurring after each groundwater input zone. Moreover, lateral C gas inputs from riparian soils were the major source of CO2 and CH4 to the stream. DOC mineralization and CH4 oxidation within the stream accounted for 17–51% of stream CO2 emissions, and this contribution was the greatest during relatively higher flows. Overall, our results illustrate how the nature and arrangement of groundwater flowpaths can organize patterns of stream C concentrations, transformations, and emissions by acting as a direct source of gases and by supplying organic substrates that fuel aquatic metabolism. Hence, refined assessments of how catchment structure influences the timing and magnitude of groundwater–stream connections are crucial for mechanistically understanding and scaling C evasion rates from headwaters.
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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) | 0.000 | 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".