Sediment chemistry controls methane emissions from lake littoral zones
Bibliographic record
Abstract
Abstract Methane emissions from the nearshore zones of lakes are relatively large but can vary by several orders of magnitude. Here we compared predictions for how sediment chemistry and microbial communities influenced methane emissions from 19 littoral sites in the UK varying in organic matter sources and microbial composition. Our approach was to compare multiple predictions to explain methane fluxes from sediment chemistry and microbial composition using path analysis. We found that the prediction that organic matter composition, namely the concentration of polyphenolics, controls methane emissions by changing electrochemical conditions to favour certain methanogen taxa was hundreds of times better supported than predictions involving abundances of all methanogens and methanotrophic bacteria, methanogen diversity, or other physicochemical conditions. Diffusive CH 4 fluxes were estimated to increase by 3.1– to 16.6-times (95% confidence interval) with increasing polyphenolic concentrations, almost entirely because they lower reduction-oxidation potentials that shift methanogen composition towards widespread taxa positively associated with methanogenesis. Rather than strongly inhibiting methane-producing microorganisms, our results suggest polyphenolics change reduction-oxidation potentials to favour acetoclastic and methylotrophic methanogens. These results help explain conflicting evidence about the responses of methane to sediment chemistry and can improve future predictions of aquatic carbon cycling. Manuscript Highlights Polyphenolics predicted nearshore CH 4 fluxes better than other environmental factors CH 4 fluxes increased with polyphenolics that lowered redox to favour methanogenesis We help explain conflicting responses of CH 4 to variation in sediment chemistry
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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) | 0.001 | 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".