First Pan-Arctic Assessment of Dissolved Organic Carbon inPermafrost-Region Lakes
Bibliographic record
Abstract
Abstract. Lakes in permafrost regions are dynamic landscape components and play an important role for climate change feedbacks. Lake processes such as mineralization and flocculation of dissolved organic carbon (DOC), one of the main carbon fractions in lakes, contribute to the greenhouse effect and are part of the global carbon cycle. These processes are in focus of climate research but studies so far are limited to specific study regions. In our synthesis, we analysed 2,167 water samples from 1,833 lakes across the Arctic in permafrost regions of Alaska, Canada, Greenland, and Siberia to provide first pan-Arctic insights for linkages between DOC concentrations and the environment. Using published data and unpublished datasets from the author team we report regional DOC differences linked to latitude, permafrost zones, ecoregions, geology, near-surface soil organic carbon contents, and ground ice classification of each lake region. The lake DOC concentrations in our dataset range from 0 mg L−1 to 1,130 mg L−1 (10.8 mg L−1 median DOC concentration). Regarding the permafrost regions of our synthesis, we found median lake DOC concentrations of 12.4 mg L−1 (Siberia), 12.3 mg L−1 (Alaska), 10.3 mg L−1 (Greenland), and 4.5 mg L−1 (Canada). Our synthesis shows a significant relationship of lake DOC concentration and ecoregion of the lake. We found higher lake DOC concentrations in boreal permafrost sites compared to tundra sites. About 22 % of the lakes in our extensive dataset are located in regions with ice-rich syngenetic permafrost deposits (yedoma). Yedoma contains large amounts of easily erodible organic carbon and we found significantly higher DOC concentrations in yedoma lakes compared to non-yedoma lakes. Compared to previous studies we found a weak significant relationship of soil organic carbon content and lake DOC concentration as well as between ground-ice content and lake DOC. Our pan-Arctic dataset shows that the DOC concentration of a lake strongly depends on its environmental properties, especially on permafrost extent and ecoregion, as well as vegetation, which is the most important driver of lake DOC in this study. This new dataset will be fundamental to quantify a pan-Arctic lake DOC pool for estimations of the impact of lake DOC on the global carbon cycle and climate change.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.000 | 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".