Turbidity Currents Can Dictate Organic Carbon Fluxes Across River‐Fed Fjords: An Example From Bute Inlet (BC, Canada)
Bibliographic record
Abstract
Abstract The delivery and burial of terrestrial particulate organic carbon (OC) in marine sediments is important to quantify, because this OC is a food resource for benthic communities, and if buried it may lower the concentrations of atmospheric CO2 over geologic timescales. Analysis of sediment cores has previously shown that fjords are hotspots for OC burial. Fjords can contain complex networks of submarine channels formed by seafloor sediment flows, called turbidity currents. However, the burial efficiency and distribution of OC by turbidity currents in river‐fed fjords had not been investigated previously. Here, we determine OC distribution and burial efficiency across a turbidity current system within Bute Inlet, a fjord in western Canada. We show that 62% ± 10% of the OC supplied by the two river sources is buried across the fjord surficial (30–200 cm) sediment. The sandy subenvironments (channel and lobe) contain 63% ± 14% of the annual terrestrial OC burial in the fjord. In contrast, the muddy subenvironments (overbank and distal basin) contain the remaining 37% ± 14%. OC in the channel, lobe, and overbank exclusively comprises terrestrial OC sourced from rivers. When normalized by the fjord’s surface area, at least 3 times more terrestrial OC is buried in Bute Inlet, compared to the muddy parts of other fjords previously studied. Although the long‐term (>100 years) preservation of this OC is still to be fully understood, turbidity currents in fjords appear to be efficient at storing OC supplied by rivers in their near‐surface deposits.
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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 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| 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".