Variations in high‐latitude riverine fluorescent dissolved organic matter: A comparison of large Arctic rivers
Bibliographic record
Abstract
Given the pace of climate change, it is important to better understand dissolved organic matter (DOM) storage and cycling in high‐latitude rivers and its subsequent export to the Arctic Ocean. To address this concern, excitation/emission matrix spectroscopy and parallel factor analysis (PARAFAC) coupled with optical indices were used to characterize the optical properties of fluorescent DOM (FDOM) and colored DOM (CDOM) in five large Arctic rivers over two seasonal cycles. Five PARAFAC components were identified and proved useful indicators for the quantitative and qualitative descriptions of DOM sources and modification processes within Arctic watersheds. In comparing Pan‐Arctic relationships between the optical properties and chemical properties of DOM, three components traced terrigenous biomarkers and two components are introduced as potential indicators for microbial processing. Conversely, no individual PARAFAC component could be directly linked to a specific plant source or river and simpler absorbance indices (i.e., a350) proved to be better suited to quantify dissolved organic carbon and lignin phenol concentrations. Differences in FDOM character between the rivers could be explained by general watershed characteristics, including vegetation, topography, and hydrology. Based on these differences, the influence of coniferous vegetation on DOM character is most apparent in the Lena and Yenisei Rivers and bog‐derived FDOM sources are more important in the Ob River. This study illustrates how increased hydrological connectivity in the Ob catchment and the abundance of lakes within the Mackenzie watershed may influence FDOM concentrations and the microbial processing of DOM within these watersheds.
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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) | 0.000 | 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".