Dissolved organic matter discharge in the six largest arctic rivers-chemical composition and seasonal variability
Bibliographic record
Abstract
The vulnerability of the Arctic to climate change has been realized due to\ndisproportionately large increases in surface air temperatures which are not uniformly\ndistributed over the seasonal cycle. Effects of this temperature shift are widespread in\nthe Arctic but likely include changes to the hydrological cycle and permafrost thaw,\nwhich have implications for the mobilization of organic carbon into rivers. The focus of\nthis research was to describe the seasonal variability of the chemical composition of\ndissolved organic matter (DOM) in the six largest Arctic rivers (Yukon, Mackenzie, Ob,\nYenisei, Lena and Kolyma) using optical properties (UV-Vis Absorbance and\nFluorescence) and lignin phenol analysis. We also investigated differences between\nrivers and how watershed characteristics influence DOM composition.\nDissolved organic carbon (DOC) concentrations followed the hydrograph with\nhighest concentrations measured during peak river flow. The chemical composition of\npeak-flow DOM indicates a dominance of freshly leached material with elevated\naromaticity, larger molecular weight, and elevated lignin yields relative to base-flow\nDOM. During peak flow, soils in the watershed are still frozen and snowmelt water\nfollows a lateral flow path to the river channels. As the soils thaw, surface water\npenetrates deeper into the soil horizons leading to lower DOC concentrations and likely\naltered composition of DOM due to sorption and microbial degradation processes. The\nsix rivers studied here shared a similar seasonal pattern and chemical composition.\nThere were, however, large differences between rivers in terms of total carbon discharge\nreflecting the differences in watershed characteristics such as climate, catchment size, river discharge, soil types, and permafrost distribution. The large rivers (Lena, Yenisei),\nwith a greater proportion of permafrost, exported the greatest amount of carbon. The\nKolyma and Mackenzie exported the smallest amount of carbon annually, however, the\ndischarge weighted mean DOC concentration was almost 2-fold higher in the Kolyma,\nagain, indicating the importance of continuous permafrost. The quality and quantity of\nDOM mobilized into Arctic rivers appears to depend on the relative importance of\nsurface run-off and extent of soil percolation. The relative importance of these is\nultimately determined by watershed characteristics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 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 teacher head, 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".