A critical look at the criticality of sediment-propelled turbidity currents
Bibliographic record
Abstract
In nature turbidity currents are common, and in the deep marine are the principal sediment transporting agents that build up the largest sediment accumulations on Earth. However these powerful currents are notoriously destructive, and as a consequence have been largely unexplored. Accordingly, researchers have turned to analyzing them in the lab. Here it is easy to form a suspended-sediment-propelled turbidity current but because of their inherently dense nature these flows are not amenable to conventional sampling instrumentation. As a consequence most researchers have used variably concentrated saline currents with the implicit assumption, or explicit statement, that they faithfully mimic the dynamics of sediment-propelled currents. Based on that work, two end member kinds of (saline) currents were identified, and differentiated on the basis of flow criticality: subcritical flows have a high velocity maximum below which density is vertically uniform (“plug-like”) and above which density decreases rapidly and shows minimal mixing with the overlying fluid; conversely, the velocity maximum of supercritical flows is located at the base of the current, density decreases exponentially away from the bed, and the current as a whole shows extensive vertical mixing. The question, therefore, is under similar hydraulic conditions, are the velocity and density profiles in saline currents representative of those formed in sediment-propelled turbidity currents? In this study, we created a variety of turbidity currents of varying velocity and sediment concentration. However unlike these other studies, this is the first to accurately measure the density profile (here a surrogate for sediment concentration) in natural, sediment-transporting turbidity currents using a medical grade CT scanner. Given similar hydraulic conditions, the two end-member profiles are controlled exclusively by particle size; specifically the velocity, density and mixing characteristics of fine grained runs were similar to subcritical saline flows whereas coarse-grained runs were similar to supercritical saline flows. Such differences in density and velocity structure will profoundly influence the vertical distribution of momentum through the current, and the degree of vertical mixing, which has important implication for the conservation of flow energy, and therefore run out distance of turbidity currents in the deep sea.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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".