The importance of anaerobic oxidation of methane in thermokarst lakes
Bibliographic record
Abstract
About 40% of the annual methane emissions originate from natural, non-anthropogenic sources. These include mainly freshwater sediments, in which significant increase in methane emissions has been observed throughout the past decades with the ongoing global temperature rise. Thermokarst lakes, formed by abrupt thawing of permafrost, play a significant role in this observed increase in methane emissions. However, methane production rates and natural consumption controls there are not well constrained, as well as their response to global warming. We explore the rates and mechanisms of methane production and anaerobic oxidation (AOM) processes several interior Alaska thermokarst lakes, which formed and continue to expand as a result of ice-rich permafrost thaw. This is mainly through geochemical and microbial profiles combined with slurry incubation experiments with labeled isotopes, potential electron acceptors and several inhibitors in different temperatures. Our manipulated experiments shed insight on the controls of methanogenesis onset and the mechanisms of both methanogenesis and AOM. Direct rate measurements using two isotope methods and modeling provide robust rate estimations for methanogenesis and AOM. They indicate that the role of AOM in these lakes is less significant than previous estimations, and that AOM will probably not attenuate the methanogenesis increase in a warmer climate.
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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".