Signatures of Arctic Change: Molecular‐Level Composition and Bioavailability of Shifting Dissolved Organic Matter Sources
Bibliographic record
Abstract
Abstract The Arctic is experiencing unprecedented rates of climate change, leading to numerous disturbances on the terrestrial landscape, including shrubification, increased frequency of wildfires, and permafrost thaw. These changes may impact the mobilization of terrestrial organic carbon into Arctic rivers and are hypothesized to lead to distinct alterations to the molecular composition and thus the reactivity of riverine dissolved organic matter (DOM). To understand how these three major perturbations may impact DOM dynamics in Arctic fluvial and coastal systems, we examined the concentration and bioavailability of dissolved organic carbon (DOC) together with the molecular‐level DOM composition of different source endmember leachates from the Yukon River watershed using biodegradation incubation experiments and Fourier transform ion cyclotron resonance mass spectrometry (FT‐ICR MS). Simulated climate‐related landscape perturbations generally led to increased leachate DOC concentrations. Incubations demonstrated that the biodegradability of leachate DOC was lowest for vegetation endmembers, particularly for shrubs (12.3% DOC loss), and highest for thawing Yedoma permafrost (64.9% loss) and organic‐rich tundra soil (70.9% loss). FT‐ICR MS highlighted that aliphatic and high‐H/C molecular formulas were preferentially biodegraded, whereas condensed aromatic and polyphenolic compounds were relatively enriched post‐biodegradation in all endmember leachates. Together these findings suggest that with continued climate change and landscape perturbation, larger amounts of less bioavailable DOC will be mobilized into Arctic rivers leading to higher relative amounts of highly aromatic, biologically stable DOM being exported into receiving ecosystems and the Arctic Ocean, potentially altering the rates and mechanisms of carbon turnover in the coastal zone.
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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 | 0.000 | 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) | 0.000 | 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".