The role of wind-wave related processes in redistributing river-derived terrigenous sediments in Lake Turkana: A modelling study
Bibliographic record
Abstract
A complete annual cycle of the dynamics of fine-grained sediment supplied by the Omo and smaller rivers is simulated for Lake Turkana, one of the world’s large lakes, with the hydrodynamic, wave and sediment transport model Delft3D. The model is forced with river liquid and solid discharge and wind data in order to simulate cohesive sediment transport and resuspension. It simulates stratification due to salinity, wave generation and dissipation, and sediment advection and resuspension by waves and currents, with multiple cohesive sediment fractions. A comparison of the simulation results with remotely-sensed imagery and with available in-situ sediment deposition rates validates the model. By devising simulation scenarios in which certain processes were switched on or off, we investigated the contribution of waves, wind-induced surface and bottom currents, salinity-induced stratification and river jet, in resuspending and transporting fine sediments in the lake basin. With only the wind or river influence, most of the sediment deposition occurs in the first 10 km off the Omo River mouth and at a depth < 10 m. When waves are switched on, increased bed shear stresses resuspend most of the fine sediments, that are then deposited further and deeper in the first 30 km, in water depths > 30 m. This study sheds new light on sediment transport in Lake Turkana and in great lakes in general, favouring the view that wind-waves can be the main agent that transports sediment away from river mouths and to deeper areas, as opposed to river-plume or gravity-driven transport.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".