Greenland Ice Sheet exports labile organic carbon to the Arctic oceans
Bibliographic record
Abstract
Abstract. Runoff from small glacier systems contains dissolved organic carbon (DOC), rich in protein-like, low molecular weight (LMW) compounds, designating glaciers as an important source of bioavailable carbon for downstream heterotrophic activity. Fluxes of DOC and particulate organic carbon (POC) exported from large Greenland catchments, however, remain unquantified, despite the Greenland Ice Sheet (GrIS) being the largest source of global glacial runoff (ca. 400 km3 yr−1). We report high and episodic fluxes of POC and DOC from a large (1200 km2) GrIS catchment during contrasting melt seasons. POC dominates organic carbon (OC) export (70–89% on average), is sourced from the ice sheet bed and contains a significant bioreactive component (9% carbohydrates). A major source for the "bioavailable" (free carbohydrates) LMW-DOC fraction is microbial activity on the ice sheet surface, with some further addition of LMW-DOC to meltwaters by biogeochemical processes at the ice sheet bed. The bioavailability of the exported DOC (30–58%) to downstream marine microorganisms is similar to that reported from other glacial watersheds. Annual fluxes of DOC and free carbohydrates during two melt seasons were similar, despite the ~ 2 fold difference in runoff fluxes, suggesting production-limited DOC sources. POC fluxes were also insensitive to an increase in seasonal runoff volumes, indicating supply-limitation of suspended sediment in runoff. Scaled to the GrIS, the combined DOC and POC fluxes (0.13–0.17 Tg C yr−1 DOC, 0.36–1.52 Tg C yr−1 mean POC) are of a similar order of magnitude to a large Arctic river system, and hence represent an important OC source to the North Atlantic, Greenland and Labrador Seas.
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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 | 0.001 |
| 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) | 0.001 | 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".