Intrusions of sediment laden fluids into density stratified water columns can be an unrecognized source of mixing in many lakes.
Bibliographic record
Abstract
When a sediment laden river flows into a stratified water body, the water mass can either intrude as an overflow, interflow, or underflow depending upon the density contrast between the river and the lake. If the river is sufficiently warm or fresh to compensate for the additional mass of sediment, an overflow results, below which convective sedimentation occurs. If the sediment load is sufficiently high, then an underflow initially occurs, from which the warm/fresh interstitial material can subsequently loft as sedimentation reduces the initial density. Such convection can even potentially overturn the water column stratification if there is a very fresh, but very high sediment load turbidity current. For intermediate cases, an interflow can occur. Here it is possible for both lofting and sediment driven convection to occur above and below the pycnocline. All these different regimes can be described in terms of two dimensionless parameters: RS and RA, which are ratios that compare the density contrast due to sediment between the river and the upper layer with the density contrast between the upper and lower layers and the density contrast between the river and upper layer, respectively. We used laboratory experiments to describe the vigour of convection in terms of these dimensionless parameters, which then allows the behaviour in various rivers inflows into lakes to be predicted. We also apply our observations to predict how a turbidity current could lead to lofting and possible overturn of the stratification of meromictic Lake Kivu.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".