Dissolved organic carbon (DOC) in Arctic ground ice
Bibliographic record
Abstract
Abstract. Thermal permafrost degradation and coastal erosion in the Arctic remobilize substantial amounts of organic carbon (OC) and nutrients which have accumulated in late Pleistocene and Holocene unconsolidated deposits. Permafrost vulnerability to thaw subsidence, collapsing coastlines and irreversible landscape change are largely due to the presence of large amounts of massive ground ice such as ice wedges. However, ground ice has not, until now, been considered to be a source of dissolved organic carbon (DOC), dissolved inorganic carbon (DIC) and other elements which are important for ecosystems and carbon cycling. Here we show, using biogeochemical data from a large number of different ice bodies throughout the Arctic, that ice wedges have the greatest potential for DOC storage, with a maximum of 28.6 mg L−1 (mean: 9.6 mg L−1). Variation in DOC concentration is positively correlated with and explained by the concentrations and relative amounts of typically terrestrial cations such as Mg2+ and K+. DOC sequestration into ground ice was more effective during the late Pleistocene than during the Holocene, which can be explained by rapid sediment and OC accumulation, the prevalence of more easily degradable vegetation and immediate incorporation into permafrost. We assume that pristine snowmelt is able to leach considerable amounts of well-preserved and highly bioavailable DOC as well as other elements from surface sediments, which are rapidly frozen and stored in ground ice, especially in ice wedges, even before further degradation. We found that ice wedges in the Yedoma region represent a significant DOC (45.2 Tg) and DIC (33.6 Tg) pool in permafrost areas and a freshwater reservoir of 4200 km2. This study underlines the need to discriminate between particulate OC and DOC to assess the availability and vulnerability of the permafrost carbon pool for ecosystems and climate feedback upon mobilization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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 teacher head, 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".