Dissolved and Particulate Organic Carbon in Icelandic Proglacial Streams: A First Estimate
Bibliographic record
Abstract
Here for the first time, we analyze the concentration of dissolved (DOC) and particulate organic carbon (POC), as well as its optical properties (absorbance and fluorescence) from several proglacial streams across Iceland, the location of Europe’s largest non-polar ice cap. We found high spatial variability of DOC concentrations and dissolved organic matter (DOM) composition during peak melt, sampling 13 proglacial streams draining the 5 main Icelandic glaciers. Although glacial-derived organic matter (OM) was dominated by proteinaceous florescence, organic matter composition was variable among glaciers, often exhibiting relatively higher aromatic content and increased humification (based on absorbance and fluorescence measurements) closer to the glacier terminus, modulated by the presence of glacial lakes. Additional sampling locations the in flow path of the river Hvitá revealed that while POC concentrations decreased downstream, DOC concentrations and the autochthonous fraction of OM increased, suggesting the reworking of the organic carbon by microbial communities, with likely implications for downstream ecosystems as glaciers continue to melt. Based on our measured DOC concentrations ranging from 0.11 mg·L−1 to 0.94 mg·L−1, we estimate a potential annual carbon release of 0.008 ± 0.002 Tg·C·yr−1 from Icelandic glaciers. This non-conservative first estimate serves to highlight the potentially significant contribution of Icelandic pro-glacial streams to the global carbon cycle and the need for the quantification and determination of the spatio-temporal variation of DOC and POC fluxes and their respective drivers, particularly in light of increased rates of melting due to recent trends in climatic warming.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".