Controls on upstream-migrating bed forms in sandy submarine channels
Bibliographic record
Abstract
Abstract Submarine channels parallel river channels in their ability to transport sediment. However, in contrast to rivers, sediment transport and bed-form development in submarine channels are less well understood. Many steep (>1°), sandy submarine channels are dominated by upstream-migrating bed forms. The flow conditions required to form these upstream-migrating bed forms remain debated because the interactions between turbidity currents and active bed forms are difficult to measure directly. Consequently, we used a depth-resolved numerical model to test the role of flow parameters that are hypothesized to control the formation of upstream-migrating bed forms in submarine channels. While our modeling results confirmed the importance of previously identified flow parameters (e.g., densiometric Froude number), we found that basal sediment concentration in turbidity currents is the strongest predictor of upstream-migrating bed-form formation. Our model shows how locally steep gradients enable high sediment concentrations (average >5 vol%) in the basal parts of flows, which allow the development of cyclic step instabilities and their associated bed forms. This new insight explains the previously puzzling observation that upstream-migrating bed forms are abundant in proximal, steep, sandy reaches of submarine channels, while their occurrence becomes more intermittent downslope.
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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 | 0.000 | 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) | 0.001 | 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".