Dissolved organic carbon in thermokarst lakes
Bibliographic record
Abstract
The Arctic is warming faster compared to other regions on Earth, resulting in significant permafrost thaw, a process that causes the release of stored organic carbon into aquatic systems, particularly thermokarst lakes. These dynamic systems, formed by the degradation of ice-rich permafrost, play an important role in the global carbon cycle. Dissolved organic carbon (DOC), a major carbon fraction in thermokarst lakes, can either be sequestered in lakes sediments or degraded through microbial and photochemical processes and further released into the atmosphere as carbon dioxide (CO2) and methane (CH4), two potent greenhouse gases. The knowledge on processes of DOC in thermokarst lakes is essential for predicting their impact on climate change. This thesis aims to address gaps in our understanding of DOC dynamics in Arctic thermokarst lakes. Therefore, the research objectives include: (1) identifying linkages between DOC concentrations and environmental characteristics on a pan- Arctic scale, (2) investigating landscape components in a yedoma catchment and their influence on DOC fluxes in surface waters, and (3) analyzing the impacts of lake changes on DOC concentrations at a regional scale. To achieve this, we combined data synthesis, field investigations, hydrochemical analysis, remote sensing, and geospatial analysis. We conducted measurements of DOC concentrations, electrical conductivity, pH, stable water isotopes, radiocarbon dating, and CH4 concentrations from thermokarst lakes of various size and lagoons to analyze DOC sources, pathways, and transformations. Through our extensive dataset of 2,167 water samples from 1,830 lakes across Alaska, Canada, Greenland, and Siberia, we demonstrated significant regional variations in DOC concentrations. These variations are linked to latitude, permafrost extent, ecoregions, geology, and soil organic carbon content. In our detailed study of a small lake catchment in the Lena River Delta, we identified the degradation of old yedoma permafrost as a major DOC source. Additionally, we investigated DOC and CH4 dynamics in relation to lake change processes (expansion vs. shrinking) and thermokarst lake-ice regimes due to permafrost thaw, discussing how climate warming and shifting lake-ice regimes influence carbon emissions from these lakes. This thesis provides a synthesis on the complex interaction of climatic, biological, and hydrological factors affecting DOC concentrations in Arctic thermokarst lakes. As permafrost thaw accelerates with future climate warming, an increase in DOC concentrations and further in greenhouse gas emissions can be expected. By integrating large-scale pan-Arctic, regional, and local studies, this thesis provides valuable insights into DOC variability, carbon cycling, and the challenges in predicting long-term DOC trends in the rapidly changing Arctic.
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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 | 0.005 |
| Science and technology studies | 0.001 | 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".