Fluvial versus coastal input of permafrost organic carbon - insights from the Canadian Beaufort Sea
Bibliographic record
Abstract
Around 65% of the Arctic coastline consists of permafrost. Rising global air temperatures cause these permafrost grounds to thaw which leads to the release of organic matter and sediments into the coastal ocean. This influences coastal ecosystem functioning and may further enhance atmospheric warming due to greenhouse gas emissions when the released carbon decomposes. Permafrost organic carbon enters the coastal ocean either through coastal erosion or through fluvial discharge and both fluxes are expected to increase in the future. The Canadian Beaufort Sea receives material from both sources, the region has some of the highest erosion rates in the Arctic and receives additional input through the Mackenzie River, the largest sediment supplier to the Arctic Ocean. To reliably estimate the current and future impacts of permafrost carbon on the coastal ocean and its potential climate feedback, we need to distinguish between these two sources whose fluxes may respond differently to ongoing Arctic change. However, we still lack reliable methods to do so.Here we propose a multiproxy approach to distinguish between sources of permafrost organic carbon by combining organic with inorganic geochemical tracers, grain size and grain shape data on a land-coast-ocean transect in the Mackenzie River Delta. The combined data pinpoints to differences in sediment source, composition, degradation, and transport pathways of both fluvially-discharged and coastally-eroded carbon. Degradation processes of organic and inorganic matter are tightly coupled, but do change within different environments (salinity, energy regimes). By combining degradation state (stable isotopes) and transport indicators (such as grain roundness) with source region tracers (XRF, radiogenic isotopes) we aim to gain insights into the interaction, transition, and origin of this different kind of matter. If successful, this approach can be applied and compared to other Arctic delta environments to fully understand the impacts of increased permafrost thaw and changing river discharge patterns on the coastal Arctic Ocean.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".