Molecular Signatures of Glacial Dissolved Organic Matter From Svalbard and Greenland
Bibliographic record
Abstract
Abstract Glaciers and ice sheets cover over 10 % of Earth's land surface area and store a globally significant amount of dissolved organic matter (DOM), which is highly bioavailable when exported to proglacial environments. Recent rapid glacier mass loss is hypothesized to have increased fluxes of DOM from these environments, yet the molecular composition of glacially derived DOM has only been studied for a handful of glaciers. We determine DOM composition using ultrahigh resolution mass spectrometry from a diverse suite of Arctic glacial environments, including time series sampling from an ice sheet catchment in Greenland (Russell Glacier) and outflow from valley glaciers in catchments with varying degrees of glacial cover in Svalbard. Samples from the Greenland outflow time series exhibited a higher degree of similarity than glacier outflow between glaciers in Svalbard; however, supraglacial meltwater samples from Greenland and Svalbard were more similar to each other than corresponding glacial outflow. Outflow from Russell Glacier was enriched in polyphenolic formulae, potentially reflecting upstream inputs from plants and soils, or inputs from paleosols overridden by the ice sheet, whereas Svalbard rivers exhibited a high level of molecular richness and dissimilarity between sites. When comparing DOM compositional analyses from other aquatic systems, aliphatic, and peptide‐like formulae appear particularly abundant in supraglacial meltwater, suggesting the DOM quickly metabolized in previous incubations of glacial water originates from energy‐rich supraglacial sources. Therefore, as glaciers lose mass across the region, higher‐quality fuel for microbial degradation will increase heterotrophy in coastal systems with ramifications for carbon cycling.
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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) | 0.000 | 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".