Great Slave Lake as a modulator of dissolved organic carbon fluxes from the Mackenzie River watershed to the Arctic Ocean
Bibliographic record
Abstract
Understanding dissolved organic carbon (DOC) fluxes in boreal freshwater systems is critical for constraining global carbon budgets and anticipating climate impacts. This study provides a multi-decadal (2000–2022) assessment of DOC fluxes in the Upper Mackenzie River watershed, focusing on Great Slave Lake (GSL)—a key hydrological node in North America’s largest Arctic-draining river system. Using field observations, remote sensing, and hydrological modeling, we estimated DOC fluxes into and out of GSL and calculated a lake-wide DOC budget. Results reveal strong spatial heterogeneity in DOC fluxes among tributaries, primarily driven by discharge and terrain characteristics such as wetland extent, slope, and soil organic carbon content. Wetland-rich, low-relief basins delivered the highest DOC concentrations, while Canadian Shield rivers contributed the lowest. Daily DOC concentrations were estimated with reasonable accuracy (±14%) from watershed attributes and discharge. The Slave River accounted for ~70% of total DOC input to GSL, followed by the Hay River (~10%). GSL removed >30% of the incoming DOC, highlighting its role as a net DOC sink and modulator of DOC fluxes to the Arctic Ocean. This regulatory function helps explain the lower DOC concentrations and distinct chemical composition of the Mackenzie River relative to major Siberian rivers, which lack large lake influences. By altering DOC concentrations and composition, GSL can influence Arctic-Ocean ecosystems and the long-term fate of terrigenous DOC. As boreal warm seasons intensify, GSL’s modulatory role will likely become increasingly important. These findings underscore the importance of incorporating lake-specific processes into assessments of northern freshwater carbon dynamics.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".