The formation and evolution of submarine headless channels
Bibliographic record
Abstract
The scale of submarine channels can rival or exceed those formed on land and they form many of the largest sedimentary deposits on Earth. Turbidity currents that carve submarine channels pose a major hazard to offshore cables and pipelines, and transport globally significant amounts of organic carbon. Alongside the primary channels, many systems also exhibit a range of headless channels, which often abruptly terminate at steep headscarps. These enigmatic features are widespread in lakes and ocean floors, either as branches off the main submarine channel thalweg or as isolated secondary channels. Prior research has proposed that headless channels may be associated with early and incipient stages of channel development, but their formation and evolution remain poorly understood. Here, we investigate the morphology, origin and development of headless channels by examining repeat bathymetric surveys spanning a period from 1986 to 2018, in Bute Inlet, Canada. We show how channel switching processes, the extension of turbidity currents across distal fans, along with overbanking turbidity currents, are able to initiate headless channels in submarine settings. We discuss how the evolution of headless channels plays an important role in shaping submarine channels, promoting channel extension and modifying the overall longitudinal profile, as well as impacting the character of sedimentary records in channel-lobe transition zones.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 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".