Large Variability in the Radiocarbon Signature of Greenhouse Gases From Incubations of Thermokarst Lake Sediments Linked to Methane Production Rates and CH<sub>4</sub>:CO<sub>2</sub> Ratios
Bibliographic record
Abstract
Abstract Thermokarst lakes have the potential to contribute to permafrost climate feedbacks through large methane (CH4) and carbon dioxide (CO2) emissions. However, it is not fully understood how the mobilization of aged carbon from permafrost contributes to greenhouse gas (GHG) production in lake sediments. We carried out anaerobic incubations of thermokarst lake sediments to better understand the factors influencing CH4 and CO2 radiocarbon (14C) values. We observed an unexpectedly large variability of CO2 and CH4 radiocarbon values (Δ14C‐GHG; −790 to +70‰) across incubation experiments. This variation is much greater than the 14C variation in bulk sediment organic matter (OM) (Δ14C‐OM, −400 to −40‰) while Δ14CH4 and Δ14CO2 were close to each other and were strongly positively correlated. We observed much lower Δ14C‐GHG when sediments were stored for a longer period (11 vs. 3 months) prior to incubation, likely because of a loss of young and labile dissolved organic carbon during storage. Δ14C values of both GHGs were strongly positively correlated with net CH4 production rates and CH4:CO2 ratios, implying that when younger OM is decomposed, CH4 production is faster, although net CO2 production rates were not correlated with Δ14C‐GHG. Δ14CH4 was also negatively correlated with sediment OC:N ratios, suggesting that greater contributions of carbon‐rich peat OM to sediments is associated with the respiration of older carbon. Our results indicate that Δ14C‐GHG in thermokarst lake sediments is not strongly controlled by overall sediment Δ14C‐OM, but is linked to the availability of labile younger C pools.
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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".