Controls on Riverine Dissolved Organic Matter Composition Across an Arctic‐Boreal Latitudinal Gradient
Bibliographic record
Abstract
Abstract Climatic changes are transforming northern high‐latitude watersheds as permafrost thaws and vegetation and hydrology shift. These changes have implications for the source and reactivity of riverine dissolved organic matter (DOM), and thus biogeochemical cycling, across northern high‐latitude systems. In this study, we use a latitudinal gradient from the interior to the North Slope of Alaska to evaluate seasonal and landscape drivers of DOM composition in this changing Arctic environment. To assess DOM source and composition, we used absorbance and fluorescence spectroscopy to measure DOM optical properties, lignin biomarker analyses to evaluate vascular plant contribution to the DOM pool, and Fourier transform ion cyclotron resonance mass spectrometry (FT‐ICR MS) to assess DOM compositional changes. We found that seasonal inputs of DOM at elevated discharge during the freshet were typically more aromatic in nature with higher lignin concentrations and carbon‐normalized yields. Landscape characteristics were a major control on dissolved organic carbon (DOC) yields and DOM composition. More northern watersheds, which were steeper, underlain by continuous permafrost, and exhibited a mix of barren and lichen/moss vegetation cover, exported less DOC with relatively more aliphatic DOM compared to more southern basins. Watersheds with deeper active layers exported DOM that was more aromatic with higher polyphenolic and condensed aromatic relative abundances and lignin yields, likely sourced from shallow subsurface flow during high discharge periods. However, contributions from deeper groundwater to streamflow is expected to increase, which would increase interactions of groundwater with mineral soils and decrease aromatic DOM contributions during periods of low discharge.
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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".