Processes That Initiate Turbidity Currents and Their Influence on Turbidites: A Marine Geology Perspective
Bibliographic record
Abstract
Abstract How the processes that initiate turbidity currents influence turbidite deposition is poorly understood, and many discussions in the literature rely on concepts that are overly simplistic. Marine geological studies provide information on the initiation and flow path of turbidity currents, including their response to gradient. In case studies of late Quaternary turbidites on the eastern Canadian and western U.S. margins, initiation processes are inferred either from real-time data for historical flows or indirectly from the age and contemporary paleogeography, erosional features, and depositional record. Three major types of initiation process are recognized: transformation of failed sediment, hyperpycnal flow from rivers or ice margins, and resuspension of sediment near the shelf edge by oceanographic processes. Many high-concentration flows result from hyperpycnal supply of hyperconcentrated bedload, or liquefaction failure of coarse-grained sediment, and most tend to deposit in slope conduits and on gradients < 0.5° at the base of slope and on the mid fan. Highly turbulent flows, from transformation of retrogressive failures and from ignitive flows that are triggered by oceanographic processes, tend to cannibalize these more proximal sediments and redeposit them on lower gradients on the basin plain. Such conduit flushing provides most of the sediment in large turbidites. Initiation mechanism exerts a strong control on the duration of turbidity flows. In most basins, there is a complex feedback between different types of turbidity-current initiation, the transformation of the flows, and the associated slope morphology. As a result, there is no simple relationship between initiating process and type of deposit.
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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 | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".