Fill, flush or shuffle: How is sediment carried through submarine channels to build lobes?
Bibliographic record
Abstract
Submarine channels are the primary conduits for land-derived material, including organic carbon, pollutants, and nutrients, into the deep-sea. The flows (turbidity currents) that traverse these systems can pose hazards to seafloor infrastructure such as cables and pipelines. Here we use a novel combination of repeat seafloor surveys and turbidity current monitoring along a 50 km-long submarine channel in Bute Inlet, British Columbia, and discharge measurements from the main feeding river. These source-to-sink observations provide the most detailed information yet on magnitude-frequency-distance relationships for turbidity currents, and the spatial-temporal patterns of sediment transport within a submarine channel-lobe system. This analysis provides new insights into mass redistribution, and particle residence times in submarine channels, as well as where particles are eventually buried and how that is recorded in the deposits. We observe stepwise sediment transport down the channel, with turbidity currents becoming progressively less frequent with distance. Most flows dissipate and deposit within the proximal (< 11 km) part of the system, whilst longer run-out flows then pick up this sediment, ‘shuffling’ it further downstream along the channel. This shuffling occurs mainly through upstream migration of knickpoints, which can generate sediment bypass along the channel over timescales of 10–100 yrs. Infrequent large events flush the channel and ultimately transport sediment onto the lobe. These flushing events can occur without obvious triggers, and thus might be internally generated. We then present the first ever sediment budget analysis of an entire submarine channel system, which shows that the river input and lobe aggradation can approximately balance over decadal timescales. We conclude by discussing the implication of this sediment shuffling for seafloor geohazards and particle burial.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 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 teacher head, 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".