Physical Factors and Microbubble Formation Explain Differences in CH4 Dynamics Between Shallow Lakes Under Alternative States
Bibliographic record
Abstract
Submerged macrophytes play a key role in maintaining clear vegetated states in shallow lakes, but their role on methane (CH4) dynamics is less explored. They might enhance methanogenesis by providing organic matter but they can also supply oxygen to the sediments increasing methanotrophy. They may also affect gas exchange by diminishing wind turbulence in the water column. We previously measured seasonal CO2 and CH4 partial pressure (pCO2 and pCH4) and diffusive fluxes from two clear vegetated and two turbid algal shallow lakes of the Pampean Plain, Argentina, and we reported that clear lakes had higher mean annual pCH4 despite states having similar mean annual CH4 diffusive flux. In this study we explore the contribution of physical and biological factors regulating surface pCH4. Mean annual CH4 diffusive fluxes and CH4 fraction of oxidation (Fox) were similar between states, implying a comparable mean annual CH4 input. kCH4 was significantly higher than kCO2, suggesting occurrence of CH4 microbubbles, yet kCH4 was higher in turbid lakes than in clear lakes, implying a higher microbubble formation in turbid lakes. Furthermore, in turbid lakes there were positive relationships between k and wind speed, and between k and pCH4, yet in clear lakes these relations were absent. Results suggest that submerged vegetation suppresses wind induced turbulence in clear vegetated lakes, decoupling kCH4 from wind and reducing microbubble formation, therefore augmenting pCH4 in their surface waters. Overall, physical rather than biological factors appear to control the observed differences in pCH4 between states.
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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 | 0.000 | 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".