Temperature, Water Depth, and Flow Velocity Are Important Drivers of Methane Ebullition in a Temperate Lowland Stream
Bibliographic record
Abstract
Abstract Streams and rivers are a well‐recognized source of methane (CH4), with high spatiotemporal variability in fluxes. However, CH4 release in form of bubbles (ebullition) is rarely included in current global CH4 emission estimates from lotic ecosystems, due to the lack of reliable models to upscale ebullition. Our study aimed to determine the importance of individual emission pathways (diffusion and ebullition) for total CH4 emissions from a lowland stream with low sediment heterogeneity and explore the relations of ebullition to environmental variables to build a stream ebullition model for this simplified system. We measured CH4 and carbon dioxide (CO2) diffusive emissions and ebullition from a temperate lowland stream in Czech Republic (Central Europe) during the ice‐free season 2021. The studied stream was a significant source of CH4 (mean 260 ± 107 mg CH4 m−2 day−1), with ebullition as a prevailing pathway of CH4 emission (mean 74 ± 7%, range 55%–85%) throughout the whole monitored period. CH4 ebullition showed a high spatiotemporal heterogeneity, with sediment temperature and water depth as the strongest predictors, followed by the interaction between flow velocity and sediment temperature. Our model explained 81% of total variance of CH4 ebullition and suggests that it is possible to model ebullitive fluxes in lowland streams with homogeneous sediments. Since CH4 was an important part of the total CO2‐equivalent emissions from the examined stream, accounting for mean (±SD) 35 ± 7.4%, and ebullition the majority of the CH4 emission, the ability to adequately model ebullition is pertinent for lowland streams.
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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 | 0.000 | 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".