Hydrologic and Landscape Controls on Dissolved Organic Matter Composition Across Western North American Arctic Lakes
Bibliographic record
Abstract
Abstract Northern high‐latitude lakes are hotspots for cycling dissolved organic carbon (DOC) inputs from allochthonous sources to the atmosphere. However, the spatial distribution of lake dissolved organic matter (DOM) is largely unknown across Arctic‐boreal regions with respect to the surrounding landscape. We expand on regional studies of northern high‐latitude DOM composition by integrating DOC concentrations, optical properties, and molecular‐level characterization from lakes spanning the Canadian Taiga to the Alaskan Tundra. Lakes were sampled during the summer from July to early September to capture the growing season. DOM became more optically processed and molecular‐level aromaticity increased northward across the Canadian Shield to the southern Arctic and from interior Alaska to the Tundra, suggesting relatively greater DOM incorporation from allochthonous sources. Using water isotopes (δ18O‐H2O), we report a weak overall trend of increasing DOC and decreasing aromaticity in lakes that were hydrologically isolated from the landscape and enriched in δ18O‐H2O, while within‐region trends were stronger and varied depending on the landscape. Finally, DOC correlated weakly with chromophoric dissolved organic matter (CDOM) across the study sites, suggesting that autochthonous and photobleached DOM were a major component of the DOC in these regions; however, some of the northernmost and wetland‐dominated lakes followed pan‐Arctic riverine DOC‐CDOM relationships, indicating strong contributions from allochthonous inputs. As many lakes across the North American Arctic are experiencing changes in temperature and precipitation, we expect the proportions of allochthonous and autochthonous DOM to respond with aquatic optical browning with greater landscape connectivity and more internally produced DOM in hydrologically isolated lakes.
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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 | 0.001 | 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".