The evolution of methane production rates from young to mature thermokarst lakes
Bibliographic record
Abstract
Abstract. Thermokarst lakes, formed by permafrost thaw in the Arctic, are hotspots for methane (CH4) and carbon dioxide (CO2) emissions, and are expected to double permafrost carbon emissions by the end of the century. While the implications of ongoing permafrost thaw on methane dynamics within these lakes have been modeled, here we provide empirical data on methane production dynamics as lakes evolve from young recently formed lakes to older lakes that have been present for hundreds of years. Sediment cores (up to 4 m long) were collected from the centers and thermokarst margins of a new thermokarst lake [Big Trail Lake (BTL), <70 years] and from an older thermokarst lake [Goldstream Lake (GSL), ~900 years] from the same interior Alaskan watershed. Highest methane production rates were observed in the uppermost sediments near the sediment-water interface at the thermokarst margins of both lakes, with a steep decrease with sediment depth into the talik. BTL exhibited elevated methane production rates, correlated with higher carbon lability for thermal induced reactions measured by Rock Eval analyses, and suggesting its potential use as a proxy for organics susceptibility for methanogenesis. In contrast, GSL displayed lower methane production rates, likely due to a longer period of organic matter degradation and reduced carbon lability. The integrated sediment-column methane production rates were similar (around 7–10 mol m-2 year-1), primarily due to the thinner talik at BTL. Our data support the predictions that formation and expansion of thermokarst lakes over the next centuries will increase methane production in newly thawed Yedoma permafrost sediments, while methane production will decrease as taliks mature and labile organic matter is used up. The positive warming effect of yedoma lake methane emissions may weaken over longer periods as the organics becomes mainly refractory, and the landscape can no longer support significant lake formation and expansion.
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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 | 0.000 |
| 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".