First source-to-sink monitoring shows dense head controls sediment flux and runout in turbidity currents
Bibliographic record
Abstract
Until recently, despite being one of the most important sediment transport phenomena on Earth, few direct measurements of turbidity currents existed. Consequently, their structure and evolution were poorly understood, particularly whether they are dense or dilute. Here, we analyze the largest number of turbidity currents monitored to date from source to sink. We show sediment transport and internal flow characteristic evolution as they runout. Observed frontal regions (heads) are fast (>1.5 m/s), thin (<10 m), dense (depth averaged concentrations up to 38% vol ), strongly stratified, and dominated by grain-to-grain interactions, or slower (<1 m/s), dilute (<0.01% vol ), and well mixed with turbulence supporting sediment. Between these end-members, a transitional flow head exists. Flow bodies are typically thick, slow, dilute, and well mixed. Flows with dense heads stretch and bulk up with dense heads transporting up to 1000 times more sediment than the dilute body. Dense heads can therefore control turbidity current sediment transport and runout into the deep sea.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".