Dynamics of methane cycling microbiome during methane flux hot moments from riparian buffer systems
Bibliographic record
Abstract
Riparian buffer systems (RBS) are a common agroforestry practice that consists of keeping a forested boundary adjacent to water bodies in agricultural landscapes, thus helping to protect aquatic ecosystems from adverse impacts. Nevertheless, despite the multiple benefits they provide, RBSs can be hotspots of methane emissions since abundant organic carbon and high-water tables are often found in these soils. In southern Ontario, Canada, the rehabilitation of Washington Creek streambank occurred in 1985. In a recent study, methane (CH4) fluxes were measured biweekly for two years (2017-2018) in four different vegetative riparian areas alongside Washington creek: a rehabilitated tree buffer (RH), a grassed buffer (GRB), an undisturbed deciduous forest (UNF), an undisturbed coniferous forest (CF), and an adjacent agricultural field (AGR) for comparison. Based on methane fluxes in 2018 and hot moments identified, we selected two dates from summer (July 04 and August 15) and use soil sampling from those days to assess the CH4 cycling microbial communities in these RBS. We used qPCR and high-throughput sequencing from both DNA and cDNA to measure the diversity and activity of the methanogen and methanotroph communities. Methanogens were abundant in all riparian soils, including the archaeal genera Methanosaeta, Methanosarcina, Methanomassiliicoccus Methanoreggula, but they were mostly active in UNF soils. Among methanotrophs, Methylocystis was the most abundant taxon in all the riparian sites, except for AGR soils where the methanotrophs community mostly comprised members of rice paddy clusters (RPCs and RPC-1) and upland soil clusters (TUSC and USCα). In summary, these results indicate that differences in CH4 fluxes between RBS at Washington creek are influenced by differences in the presence and activity of methanogens, which were higher in the deciduous forest (UNF) soils during hot moments CH4 flux, likely due to high water content in that soils
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".