How to recognize crescentic bedforms formed by supercritical turbidity currents in the geologic record: insights from active submarine channels
Bibliographic record
Abstract
Submarine channels have been important throughout geologic time for feeding globally significant volumes of sediment from land to the deep sea. Modern observations show that submarine channels can be sculpted by supercritical turbidity currents (seafloor sediment flows) that can generate upstream-migrating bedforms with a crescentic planform. In order to accurately interpret supercritical flows and depositional environments in the geologic record, it is important to be able to recognize the depositional signature of crescentic bedforms. Field geologists commonly link scour fills containing massive sands to crescentic bedforms, whereas models of turbidity currents produce deposits dominated by back-stepping beds. Here we reconcile this apparent contradiction by presenting the most detailed study yet that combines direct flow observations, time-lapse seabed mapping, and sediment cores, thus providing the link from flow process to depositional product. These data were collected within the proximal part of a submarine channel on the Squamish Delta, Canada. We demonstrate that bedform migration initially produces back-stepping beds of sand. However, these back-stepping beds are partially eroded by further bedform migration during subsequent flows, resulting in scour fills containing massive sand. As a result, our observations better match the depositional architecture of upstream-migrating bedforms produced by fluvial models, despite the fact that they formed beneath turbidity currents.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".